THE DESIGN (VERSUS THE ANALYSIS) OF EVALUATIONS FROM OBSERVATIONAL STUDIES: PARALLELS WITH THE DESIGN OF RANDOMIZED EXPERIMENTS DONALD B.
|
|
|
- Rosalyn Little
- 6 years ago
- Views:
Transcription
1 THE DESIGN (VERSUS THE ANALYSIS) OF EVALUATIONS FROM OBSERVATIONAL STUDIES: PARALLELS WITH THE DESIGN OF RANDOMIZED EXPERIMENTS DONALD B. RUBIN
2 My perspective on inference for causal effects: In randomized experiments In observational studies The RCM (Holland, 1986): 1974, 1975, 1976, 1977, 1978, 1979, 1980 Two essential parts, one optional part(*) 1. Define estimands using potential outcomes conceptual; pre data Observable results of real or hypothetical manipulations (treatments) No causation without manipulation 1975 Formal notation due to Neyman (1923) in randomized experiments Intuitive idea old, but general use of formal notation only from 1974 X, Y(0), Y(1) Prior to 1974 everyone used Y obs in observational studies Y obs,i = W i Y i (1) + (1 W i )Y i (0)
3 2. Posit assignment mechanism Reasons for missing and observed values of Y(0) and Y(1) Pr(W X, Y(0), Y(1)) Special cases much discussed Randomized experiments Neyman, Fisher (1925) Supply-Demand Haavelmo (1944) Optimizing Behavior Roy (1951) General formulation only from 1975 *3. Full probability model to predict the missing potential outcomes Pr(X, Y(0), Y(1)) Bayes formalized in 1975, 1978 #2 and #3 Posterior distribution of all causal estimands
4 Here, focus on the second step: design in observational studies. Template is generalized randomized experiment: Pr(W, X, Y(0), Y(1)) = Pr (W X) Pr( Wi = 1 Xi) = propensity scores 0 < propensity scores < 1 Design of randomized experiments is without Y(0), Y(1) Approximate randomized experiment in observational studies Blocking, matching subclassification just use X Old history of matching in statistics summarized in Structure an observational study as a broken randomized experiment, without using any values of potential outcomes Try to reconstruct underlying experiment
5 FDA analogy Pharmas do randomization over and over until they get a favorable result? Or use regression over and over on one sample until drug looks good? Instead, they carefully try to design study, followed by Specification of primary analysis & secondary supporting analyses And must live with answers that follow from proposed analyses Should we accept lower standards in social science? Objectivity can be attained in observational studies Outcome-free design before any analysis with observed outcomes
6 Very Recent Personal Examples: Tobacco litigation Value Added Assessment Peer effects of smoking Job training in Germany Treffer Illustrate with first example Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation. Health Services & Outcomes Research Methodology (2002).
7 Table 1 Variables Used in Propensity Model Description Seatbelt 5 levels of reported seat belt use Arthritis Whether reported suffering from arthritis Census Division 9 census regions Champ Insurance Diabetes Doctor ever told having diabetes Down time 6 levels of reported Down time Dump time 6 levels of reported Dump time Employment Indicating employment status each quarter English English is a primary language Retirement Indicator for Retirement status Number of Friends 7 levels measuring the number of friends Membership in Clubs 6 levels measuring memberships in clubs Education Completed years of Education HMO coverage Indicating HMO coverage each quarter High blood pressure Doctor ever told having high blood pressure Industry Code 14 Industry codes Age Age of the respondent Labor Union Indicator for a member of labor union Log Height Natural Logarithm of Height Log Weight Natural Logarithm of Weight Marital Status Marital status in each quarter Medicaid On medicaid (each quarter) Medicare On medicare (each quarter) Occupation Occupation code (13 levels) Public Assistance Other Public assistance program (each quarter) Friends over Frequency of having Friends over (7 levels) Physical Activity Indicator Variable for Physically active Population density 3 levels Poverty Status 6 levels Pregnant 1987 Pregnancy status in 1987 (women) Private Insurance Other Private Insurance (each quarter) Race 4 levels Rated Health 5-point Self rating of health status Home ownership Indicator for Owning home Rheumatism Indicator for Suffering from rheumatism Share Life Indicator variable for having somebody to share their life
8 Region MSA Risk Uninsured Veteran Incapler Agesq Educat.sq Age_wt Age_educt Age_ht Educat_wt Educat_ht Loght_logwt Loghtsq Logwtsq 4 levels of region of the country 4 levels indicating types of Metropolitan Statistical Area General risk taking attitude (5 levels) Indicator for lack insurance (each quarter) Indicator for Veteran status Survey Weight in NMES database Age*Age Education*Education Age*logwt Age*Education Age*Loght Education*Logwt Education*loght Loght*logwt Loght*loght Logwt*logwt
9 TABLE 2 ESTIMATED PROPENSITY SCORES ON THE LOGIT SCALE FOR "SMOKERS" VERSUS NEVER SMOKERS IN FULL NMES B = Bias, R = Ratio of "smoker" to never-smoker variances; also displayed is the distribution of the ratio of variances in the covariates orthogonal to the propensity score Percent of covariates with specified variance ratio after adjustment for propensity score Treated B R <1/2 >1/2 and >4/5 and >5/4 and >2 Group < 4/5 < 5/4 < 2 Current N = 3,510 Former N = 3,384 Current N = 3,434 Former N = 2,657
10 Figure 1: Propensity Scores in NMES for Smokers versus Never Smokers Controls Treated Matched Controls Current Current Former Former
11 TABLE 3 ESTIMATED PROPENSITY SCORES FROM FULL NMES ON THE LOGIT SCALE FOR "SMOKERS" VERSUS NEVER SMOKERS IN MATCHED NMES B = Bias, R = Ratio of "smoker" to never-smoker variances; also displayed is the distribution of the ratio of variances in the covariates orthogonal to the propensity score Percent of covariates with specified variance ratio after adjustment for propensity score Treated B R <1/2 >1/2 and >4/5 and >5/4 and >2 Group < 4/5 < 5/4 < 2 Current N = 3,510 Former N = 3,384 Current N = 3,434 Former N = 2,657
12 TABLE 4 ESTIMATED PROPENSITY SCORES ON THE LOGIT SCALE FOR "SMOKERS" VERSUS NEVER SMOKERS IN MATCHED NMES B = Bias, R = Ratio of "smoker" to never-smoker variances; also displayed is the distribution of the ratio of variances in the covariates orthogonal to the propensity score Percent of covariates with specified variance ratio after adjustment for propensity score Treated B R <1/2 >1/2 and >4/5 and >5/4 and >2 Group < 4/5 < 5/4 < 2 Current N = 3,510 Former N = 3,384 Current N = 3,434 Former N = 2,657
13 TABLE 5A PROPENSITY SUBCLASSIFICATION ANALYSES ON THE LOGIT SCALE FOR CURRENT VS. NEVER-SMOKER MALES IN MATCHED NMES B = Bias, R = Ratio of "smoker" to never-smoker variances; also displayed is the distribution of the ratio of variances in the covariates orthogonal to the propensity score Percent of covariates with specified variance ratio after adjustment for propensity score Number of B R <1/2 >1/2 and >4/5 and >5/4 and >2 Subclasses < 4/5 < 5/4 <
14 TABLE 5B PROPENSITY SUBCLASSIFICATION ANALYSES ON THE LOGIT SCALE FOR FORMER VS. NEVER-SMOKER MALES IN MATCHED NMES B = Bias, R = Ratio of "smoker" to never-smoker variances; also displayed is the distribution of the ratio of variances in the covariates orthogonal to the propensity score Percent of covariates with specified variance ratio after adjustment for propensity score Number of B R <1/2 >1/2 and >4/5 and >5/4 and >2 Subclasses < 4/5 < 5/4 <
15 TABLE 5C PROPENSITY SUBCLASSIFICATION ANALYSES ON THE LOGIT SCALE FOR CURRENT VS. NEVER-SMOKER FEMALES IN MATCHED NMES B = Bias, R = Ratio of "smoker" to never-smoker variances; also displayed is the distribution of the ratio of variances in the covariates orthogonal to the propensity score Percent of covariates with specified variance ratio after adjustment for propensity score Number of B R <1/2 >1/2 and >4/5 and >5/4 and >2 Subclasses < 4/5 < 5/4 <
16 TABLE 5D PROPENSITY SUBCLASSIFICATION ANALYSES ON THE LOGIT SCALE FOR FORMER VS. NEVER-SMOKER FEMALES IN MATCHED NMES B = Bias, R = Ratio of "smoker" to never-smoker variances; also displayed is the distribution of the ratio of variances in the covariates orthogonal to the propensity score Percent of covariates with specified variance ratio after adjustment for propensity score Number of B R <1/2 >1/2 and >4/5 and >5/4 and >2 Subclasses < 4/5 < 5/4 <
17 TABLE 6 WEIGHTED PROPENSITY SCORE ANALYSES BASED ON INVERSE PROBABILITIES FOR WEIGHTS IN MATCHED NMES B = Bias, R = Ratio of "smoker" to never-smoker variances; also displayed is the distribution of the ratio of variances in the covariates orthogonal to the propensity score Percent of covariates with specified variance ratio after adjustment for propensity score Treated B R <1/2 >1/2 and >4/5 and >5/4 and >2 Group < 4/5 < 5/4 < 2 Current N = 3,510 Former N = 3,384 Current N = 3,434 Former N = 2,657
18 Table 7 ESTIMATED PROPENSITY SCORES ON THE LOGIT SCALE FOR MALE CURRENT SMOKERS (N = 3510) VERSUS MALE NEVER SMOKERS IN NMES B = Bias, R = Ratio of "smoker" to never-smoker variance; also displayed is the distribution of the ratio of variances in the covariates after adjusting for the propensity score Percent of covariates with specified variance ratio after adjustment for propensity score Analysis B R <1/2 >1/2 and >4/5 and >5/4 and >2 < 4/5 < 5/4 < 2 Full N = 4,297 Matched N = 3,510 Matched K = K = K = K = K = K = K =
Job Training Partnership Act (JTPA)
Causal inference Part I.b: randomized experiments, matching and regression (this lecture starts with other slides on randomized experiments) Frank Venmans Example of a randomized experiment: Job Training
Achieving Optimal Covariate Balance Under General Treatment Regimes
Achieving Under General Treatment Regimes Marc Ratkovic Princeton University May 24, 2012 Motivation For many questions of interest in the social sciences, experiments are not possible Possible bias in
Technical Track Session I:
Impact Evaluation Technical Track Session I: Click to edit Master title style Causal Inference Damien de Walque Amman, Jordan March 8-12, 2009 Click to edit Master subtitle style Human Development Human
Matching. Quiz 2. Matching. Quiz 2. Exact Matching. Estimand 2/25/14
STA 320 Design and Analysis of Causal Studies Dr. Kari Lock Morgan and Dr. Fan Li Department of Statistical Science Duke University Frequency 0 2 4 6 8 Quiz 2 Histogram of Quiz2 10 12 14 16 18 20 Quiz2
Weighting. Homework 2. Regression. Regression. Decisions Matching: Weighting (0) W i. (1) -å l i. )Y i. (1-W i 3/5/2014. (1) = Y i.
Weighting Unconfounded Homework 2 Describe imbalance direction matters STA 320 Design and Analysis of Causal Studies Dr. Kari Lock Morgan and Dr. Fan Li Department of Statistical Science Duke University
An Introduction to Causal Analysis on Observational Data using Propensity Scores
An Introduction to Causal Analysis on Observational Data using Propensity Scores Margie Rosenberg*, PhD, FSA Brian Hartman**, PhD, ASA Shannon Lane* *University of Wisconsin Madison **University of Connecticut
Causal Inference with General Treatment Regimes: Generalizing the Propensity Score
Causal Inference with General Treatment Regimes: Generalizing the Propensity Score David van Dyk Department of Statistics, University of California, Irvine vandyk@stat.harvard.edu Joint work with Kosuke
Bayesian regression tree models for causal inference: regularization, confounding and heterogeneity
Bayesian regression tree models for causal inference: regularization, confounding and heterogeneity P. Richard Hahn, Jared Murray, and Carlos Carvalho June 22, 2017 The problem setting We want to estimate
Propensity Score Weighting with Multilevel Data
Propensity Score Weighting with Multilevel Data Fan Li Department of Statistical Science Duke University October 25, 2012 Joint work with Alan Zaslavsky and Mary Beth Landrum Introduction In comparative
DEALING WITH MULTIVARIATE OUTCOMES IN STUDIES FOR CAUSAL EFFECTS
DEALING WITH MULTIVARIATE OUTCOMES IN STUDIES FOR CAUSAL EFFECTS Donald B. Rubin Harvard University 1 Oxford Street, 7th Floor Cambridge, MA 02138 USA Tel: 617-495-5496; Fax: 617-496-8057 email: rubin@stat.harvard.edu
Technical Track Session I: Causal Inference
Impact Evaluation Technical Track Session I: Causal Inference Human Development Human Network Development Network Middle East and North Africa Region World Bank Institute Spanish Impact Evaluation Fund
Low-Income African American Women's Perceptions of Primary Care Physician Weight Loss Counseling: A Positive Deviance Study
Thomas Jefferson University Jefferson Digital Commons Master of Public Health Thesis and Capstone Presentations Jefferson College of Population Health 6-25-2015 Low-Income African American Women's Perceptions
Analyzing the Geospatial Rates of the Primary Care Physician Labor Supply in the Contiguous United States
Analyzing the Geospatial Rates of the Primary Care Physician Labor Supply in the Contiguous United States By Russ Frith Advisor: Dr. Raid Amin University of W. Florida Capstone Project in Statistics April,
Potential Outcomes and Causal Inference I
Potential Outcomes and Causal Inference I Jonathan Wand Polisci 350C Stanford University May 3, 2006 Example A: Get-out-the-Vote (GOTV) Question: Is it possible to increase the likelihood of an individuals
Statistical Models for Causal Analysis
Statistical Models for Causal Analysis Teppei Yamamoto Keio University Introduction to Causal Inference Spring 2016 Three Modes of Statistical Inference 1. Descriptive Inference: summarizing and exploring
Estimating and Using Propensity Score in Presence of Missing Background Data. An Application to Assess the Impact of Childbearing on Wellbeing
Estimating and Using Propensity Score in Presence of Missing Background Data. An Application to Assess the Impact of Childbearing on Wellbeing Alessandra Mattei Dipartimento di Statistica G. Parenti Università
Causal Inference with Big Data Sets
Causal Inference with Big Data Sets Marcelo Coca Perraillon University of Colorado AMC November 2016 1 / 1 Outlone Outline Big data Causal inference in economics and statistics Regression discontinuity
Section 9c. Propensity scores. Controlling for bias & confounding in observational studies
Section 9c Propensity scores Controlling for bias & confounding in observational studies 1 Logistic regression and propensity scores Consider comparing an outcome in two treatment groups: A vs B. In a
WORKSHOP ON PRINCIPAL STRATIFICATION STANFORD UNIVERSITY, Luke W. Miratrix (Harvard University) Lindsay C. Page (University of Pittsburgh)
WORKSHOP ON PRINCIPAL STRATIFICATION STANFORD UNIVERSITY, 2016 Luke W. Miratrix (Harvard University) Lindsay C. Page (University of Pittsburgh) Our team! 2 Avi Feller (Berkeley) Jane Furey (Abt Associates)
Investigating mediation when counterfactuals are not metaphysical: Does sunlight exposure mediate the effect of eye-glasses on cataracts?
Investigating mediation when counterfactuals are not metaphysical: Does sunlight exposure mediate the effect of eye-glasses on cataracts? Brian Egleston Fox Chase Cancer Center Collaborators: Daniel Scharfstein,
Selection on Observables: Propensity Score Matching.
Selection on Observables: Propensity Score Matching. Department of Economics and Management Irene Brunetti ireneb@ec.unipi.it 24/10/2017 I. Brunetti Labour Economics in an European Perspective 24/10/2017
New Developments in Nonresponse Adjustment Methods
New Developments in Nonresponse Adjustment Methods Fannie Cobben January 23, 2009 1 Introduction In this paper, we describe two relatively new techniques to adjust for (unit) nonresponse bias: The sample
Causal Inference Lecture Notes: Causal Inference with Repeated Measures in Observational Studies
Causal Inference Lecture Notes: Causal Inference with Repeated Measures in Observational Studies Kosuke Imai Department of Politics Princeton University November 13, 2013 So far, we have essentially assumed
Truncation and Censoring
Truncation and Censoring Laura Magazzini laura.magazzini@univr.it Laura Magazzini (@univr.it) Truncation and Censoring 1 / 35 Truncation and censoring Truncation: sample data are drawn from a subset of
CompSci Understanding Data: Theory and Applications
CompSci 590.6 Understanding Data: Theory and Applications Lecture 17 Causality in Statistics Instructor: Sudeepa Roy Email: sudeepa@cs.duke.edu Fall 2015 1 Today s Reading Rubin Journal of the American
Introduction to Statistical Inference
Introduction to Statistical Inference Kosuke Imai Princeton University January 31, 2010 Kosuke Imai (Princeton) Introduction to Statistical Inference January 31, 2010 1 / 21 What is Statistics? Statistics
Propensity Score Analysis with Hierarchical Data
Propensity Score Analysis with Hierarchical Data Fan Li Alan Zaslavsky Mary Beth Landrum Department of Health Care Policy Harvard Medical School May 19, 2008 Introduction Population-based observational
Combining Non-probability and Probability Survey Samples Through Mass Imputation
Combining Non-probability and Probability Survey Samples Through Mass Imputation Jae-Kwang Kim 1 Iowa State University & KAIST October 27, 2018 1 Joint work with Seho Park, Yilin Chen, and Changbao Wu
Causal Inference in Observational Studies with Non-Binary Treatments. David A. van Dyk
Causal Inference in Observational Studies with Non-Binary reatments Statistics Section, Imperial College London Joint work with Shandong Zhao and Kosuke Imai Cass Business School, October 2013 Outline
Lecture 4 Multiple linear regression
Lecture 4 Multiple linear regression BIOST 515 January 15, 2004 Outline 1 Motivation for the multiple regression model Multiple regression in matrix notation Least squares estimation of model parameters
STA 303 H1S / 1002 HS Winter 2011 Test March 7, ab 1cde 2abcde 2fghij 3
STA 303 H1S / 1002 HS Winter 2011 Test March 7, 2011 LAST NAME: FIRST NAME: STUDENT NUMBER: ENROLLED IN: (circle one) STA 303 STA 1002 INSTRUCTIONS: Time: 90 minutes Aids allowed: calculator. Some formulae
Econometrics I. Professor William Greene Stern School of Business Department of Economics 1-1/40. Part 1: Introduction
Econometrics I Professor William Greene Stern School of Business Department of Economics 1-1/40 http://people.stern.nyu.edu/wgreene/econometrics/econometrics.htm 1-2/40 Overview: This is an intermediate
Notes 6: Multivariate regression ECO 231W - Undergraduate Econometrics
Notes 6: Multivariate regression ECO 231W - Undergraduate Econometrics Prof. Carolina Caetano 1 Notation and language Recall the notation that we discussed in the previous classes. We call the outcome
Observational Studies and Propensity Scores
Observational Studies and s STA 320 Design and Analysis of Causal Studies Dr. Kari Lock Morgan and Dr. Fan Li Department of Statistical Science Duke University Makeup Class Rather than making you come
Use of Matching Methods for Causal Inference in Experimental and Observational Studies. This Talk Draws on the Following Papers:
Use of Matching Methods for Causal Inference in Experimental and Observational Studies Kosuke Imai Department of Politics Princeton University April 27, 2007 Kosuke Imai (Princeton University) Matching
Introduction to causal identification. Nidhiya Menon IGC Summer School, New Delhi, July 2015
Introduction to causal identification Nidhiya Menon IGC Summer School, New Delhi, July 2015 Outline 1. Micro-empirical methods 2. Rubin causal model 3. More on Instrumental Variables (IV) Estimating causal
Philip J Cimo DDS PA 650 West Bough Lane Ste #160 Houston TX
Philip J Cimo DDS PA 650 West Bough Lane Ste #160 Houston TX 770024 O: (713)464-1887 F: (713)461-0605 PATIENT INFORMATION Date: / / Patient Name: First MI Last Address: Date of Birth: Social Security #:
Introduction to Linear Regression Analysis
Introduction to Linear Regression Analysis Samuel Nocito Lecture 1 March 2nd, 2018 Econometrics: What is it? Interaction of economic theory, observed data and statistical methods. The science of testing
Instrumental Variables in Action: Sometimes You get What You Need
Instrumental Variables in Action: Sometimes You get What You Need Joshua D. Angrist MIT and NBER May 2011 Introduction Our Causal Framework A dummy causal variable of interest, i, is called a treatment,
THE SURVEY OF INCOME AND PROGRAM PARTICIPATION
THE SURVEY OF INCOME AND PROGRAM PARTICIPATION AN ANALYSIS OF THE CHARACTERISTICS OF MULTIPLE PROGRAM PARTICIPATION USING THE SURVEY OF INCOME AND PROGRAM PARTICIPATION (SIPP) No. 244 Kanin L. Reese Housing
This report details analyses and methodologies used to examine and visualize the spatial and nonspatial
Analysis Summary: Acute Myocardial Infarction and Social Determinants of Health Acute Myocardial Infarction Study Summary March 2014 Project Summary :: Purpose This report details analyses and methodologies
AGEC 661 Note Fourteen
AGEC 661 Note Fourteen Ximing Wu 1 Selection bias 1.1 Heckman s two-step model Consider the model in Heckman (1979) Y i = X iβ + ε i, D i = I {Z iγ + η i > 0}. For a random sample from the population,
T test for two Independent Samples. Raja, BSc.N, DCHN, RN Nursing Instructor Acknowledgement: Ms. Saima Hirani June 07, 2016
T test for two Independent Samples Raja, BSc.N, DCHN, RN Nursing Instructor Acknowledgement: Ms. Saima Hirani June 07, 2016 Q1. The mean serum creatinine level is measured in 36 patients after they received
BROOKINGS May
Appendix 1. Technical Methodology This study combines detailed data on transit systems, demographics, and employment to determine the accessibility of jobs via transit within and across the country s 100
The Church Demographic Specialists
The Church Demographic Specialists Easy-to-Use Features Map-driven, Web-based Software An Integrated Suite of Information and Query Tools Providing An Insightful Window into the Communities You Serve Key
Jun Tu. Department of Geography and Anthropology Kennesaw State University
Examining Spatially Varying Relationships between Preterm Births and Ambient Air Pollution in Georgia using Geographically Weighted Logistic Regression Jun Tu Department of Geography and Anthropology Kennesaw
Measuring community health outcomes: New approaches for public health services research
Research Brief March 2015 Measuring community health outcomes: New approaches for public health services research P ublic Health agencies are increasingly asked to do more with less. Tough economic times
Summary and discussion of The central role of the propensity score in observational studies for causal effects
Summary and discussion of The central role of the propensity score in observational studies for causal effects Statistics Journal Club, 36-825 Jessica Chemali and Michael Vespe 1 Summary 1.1 Background
PROPENSITY SCORE MATCHING. Walter Leite
PROPENSITY SCORE MATCHING Walter Leite 1 EXAMPLE Question: Does having a job that provides or subsidizes child care increate the length that working mothers breastfeed their children? Treatment: Working
GROWING APART: THE CHANGING FIRM-SIZE WAGE PREMIUM AND ITS INEQUALITY CONSEQUENCES ONLINE APPENDIX
GROWING APART: THE CHANGING FIRM-SIZE WAGE PREMIUM AND ITS INEQUALITY CONSEQUENCES ONLINE APPENDIX The following document is the online appendix for the paper, Growing Apart: The Changing Firm-Size Wage
Near/Far Matching. Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants
Near/Far Matching Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants Joint research: Mike Baiocchi, Dylan Small, Scott Lorch and Paul Rosenbaum Classic set
In matrix algebra notation, a linear model is written as
DM3 Calculation of health disparity Indices Using Data Mining and the SAS Bridge to ESRI Mussie Tesfamicael, University of Louisville, Louisville, KY Abstract Socioeconomic indices are strongly believed
Nemours Biomedical Research Statistics Course. Li Xie Nemours Biostatistics Core October 14, 2014
Nemours Biomedical Research Statistics Course Li Xie Nemours Biostatistics Core October 14, 2014 Outline Recap Introduction to Logistic Regression Recap Descriptive statistics Variable type Example of
Near/Far Matching. Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants
Near/Far Matching Building a Stronger Instrument in an Observational Study of Perinatal Care for Premature Infants Joint research: Mike Baiocchi, Dylan Small, Scott Lorch and Paul Rosenbaum What this talk
Use of Matching Methods for Causal Inference in Experimental and Observational Studies. This Talk Draws on the Following Papers:
Use of Matching Methods for Causal Inference in Experimental and Observational Studies Kosuke Imai Department of Politics Princeton University April 13, 2009 Kosuke Imai (Princeton University) Matching
Rockefeller College University at Albany
Rockefeller College University at Albany PAD 705 Handout: Simultaneous quations and Two-Stage Least Squares So far, we have studied examples where the causal relationship is quite clear: the value of the
Causal Inference with a Continuous Treatment and Outcome: Alternative Estimators for Parametric Dose-Response Functions
Causal Inference with a Continuous Treatment and Outcome: Alternative Estimators for Parametric Dose-Response Functions Joe Schafer Office of the Associate Director for Research and Methodology U.S. Census
Applied Microeconometrics (L5): Panel Data-Basics
Applied Microeconometrics (L5): Panel Data-Basics Nicholas Giannakopoulos University of Patras Department of Economics ngias@upatras.gr November 10, 2015 Nicholas Giannakopoulos (UPatras) MSc Applied Economics
14.74 Lecture 10: The returns to human capital: education
14.74 Lecture 10: The returns to human capital: education Esther Duflo March 7, 2011 Education is a form of human capital. You invest in it, and you get returns, in the form of higher earnings, etc...
Econometrics Problem Set 4
Econometrics Problem Set 4 WISE, Xiamen University Spring 2016-17 Conceptual Questions 1. This question refers to the estimated regressions in shown in Table 1 computed using data for 1988 from the CPS.
Chapter 11. Regression with a Binary Dependent Variable
Chapter 11 Regression with a Binary Dependent Variable 2 Regression with a Binary Dependent Variable (SW Chapter 11) So far the dependent variable (Y) has been continuous: district-wide average test score
PubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH
PubH 7470: STATISTICS FOR TRANSLATIONAL & CLINICAL RESEARCH The First Step: SAMPLE SIZE DETERMINATION THE ULTIMATE GOAL The most important, ultimate step of any of clinical research is to do draw inferences;
Estimating the Mean Response of Treatment Duration Regimes in an Observational Study. Anastasios A. Tsiatis.
Estimating the Mean Response of Treatment Duration Regimes in an Observational Study Anastasios A. Tsiatis http://www.stat.ncsu.edu/ tsiatis/ Introduction to Dynamic Treatment Regimes 1 Outline Description
II. MATCHMAKER, MATCHMAKER
II. MATCHMAKER, MATCHMAKER Josh Angrist MIT 14.387 Fall 2014 Agenda Matching. What could be simpler? We look for causal effects by comparing treatment and control within subgroups where everything... or
ANALYTIC COMPARISON. Pearl and Rubin CAUSAL FRAMEWORKS
ANALYTIC COMPARISON of Pearl and Rubin CAUSAL FRAMEWORKS Content Page Part I. General Considerations Chapter 1. What is the question? 16 Introduction 16 1. Randomization 17 1.1 An Example of Randomization
Comments on The Role of Large Scale Assessments in Research on Educational Effectiveness and School Development by Eckhard Klieme, Ph.D.
Comments on The Role of Large Scale Assessments in Research on Educational Effectiveness and School Development by Eckhard Klieme, Ph.D. David Kaplan Department of Educational Psychology The General Theme
Implementing Matching Estimators for. Average Treatment Effects in STATA
Implementing Matching Estimators for Average Treatment Effects in STATA Guido W. Imbens - Harvard University West Coast Stata Users Group meeting, Los Angeles October 26th, 2007 General Motivation Estimation
Balancing Covariates via Propensity Score Weighting
Balancing Covariates via Propensity Score Weighting Kari Lock Morgan Department of Statistics Penn State University klm47@psu.edu Stochastic Modeling and Computational Statistics Seminar October 17, 2014
OUTLINE CAUSAL INFERENCE: LOGICAL FOUNDATION AND NEW RESULTS. Judea Pearl University of California Los Angeles (www.cs.ucla.
OUTLINE CAUSAL INFERENCE: LOGICAL FOUNDATION AND NEW RESULTS Judea Pearl University of California Los Angeles (www.cs.ucla.edu/~judea/) Statistical vs. Causal vs. Counterfactual inference: syntax and semantics
Bayesian Inference for Sequential Treatments under Latent Sequential Ignorability. June 19, 2017
Bayesian Inference for Sequential Treatments under Latent Sequential Ignorability Alessandra Mattei, Federico Ricciardi and Fabrizia Mealli Department of Statistics, Computer Science, Applications, University
Lecture 1 January 18
STAT 263/363: Experimental Design Winter 2016/17 Lecture 1 January 18 Lecturer: Art B. Owen Scribe: Julie Zhu Overview Experiments are powerful because you can conclude causality from the results. In most
University of California at Berkeley Fall Introductory Applied Econometrics Final examination. Scores add up to 125 points
EEP 118 / IAS 118 Elisabeth Sadoulet and Kelly Jones University of California at Berkeley Fall 2008 Introductory Applied Econometrics Final examination Scores add up to 125 points Your name: SID: 1 1.
ESTIMATING AVERAGE TREATMENT EFFECTS: REGRESSION DISCONTINUITY DESIGNS Jeff Wooldridge Michigan State University BGSE/IZA Course in Microeconometrics
ESTIMATING AVERAGE TREATMENT EFFECTS: REGRESSION DISCONTINUITY DESIGNS Jeff Wooldridge Michigan State University BGSE/IZA Course in Microeconometrics July 2009 1. Introduction 2. The Sharp RD Design 3.
IP WEIGHTING AND MARGINAL STRUCTURAL MODELS (CHAPTER 12) BIOS IPW and MSM
IP WEIGHTING AND MARGINAL STRUCTURAL MODELS (CHAPTER 12) BIOS 776 1 12 IPW and MSM IP weighting and marginal structural models ( 12) Outline 12.1 The causal question 12.2 Estimating IP weights via modeling
Logistic regression: Why we often can do what we think we can do. Maarten Buis 19 th UK Stata Users Group meeting, 10 Sept. 2015
Logistic regression: Why we often can do what we think we can do Maarten Buis 19 th UK Stata Users Group meeting, 10 Sept. 2015 1 Introduction Introduction - In 2010 Carina Mood published an overview article
Bayesian Causal Forests
Bayesian Causal Forests for estimating personalized treatment effects P. Richard Hahn, Jared Murray, and Carlos Carvalho December 8, 2016 Overview We consider observational data, assuming conditional unconfoundedness,
Propensity Score Analysis Using teffects in Stata. SOC 561 Programming for the Social Sciences Hyungjun Suh Apr
Propensity Score Analysis Using teffects in Stata SOC 561 Programming for the Social Sciences Hyungjun Suh Apr. 25. 2016 Overview Motivation Propensity Score Weighting Propensity Score Matching with teffects
A Decision Theoretic Approach to Causality
A Decision Theoretic Approach to Causality Vanessa Didelez School of Mathematics University of Bristol (based on joint work with Philip Dawid) Bordeaux, June 2011 Based on: Dawid & Didelez (2010). Identifying
TriMatch: An R Package for Propensity Score Matching of Non-binary Treatments
TriMatch: An R Package for Propensity Score Matching of Non-binary Treatments Jason M. Bryer Excelsior College May 3, 013 Abstract The use of propensity score methods (Rosenbaum and Rubin, 1983) have become
Causal Modeling in Environmental Epidemiology. Joel Schwartz Harvard University
Causal Modeling in Environmental Epidemiology Joel Schwartz Harvard University When I was Young What do I mean by Causal Modeling? What would have happened if the population had been exposed to a instead
Have you... Unit 1: Introduction to data Lecture 1: Data collection, observational studies, and experiments. Readiness assessment
Have you... Unit 1: Introduction to data Lecture 1: Data collection, observational studies, and experiments Statistics 101 Mine Çetinkaya-Rundel January 15, 2013 been placed into a team? successfully logged
Class Notes: Week 8. Probit versus Logit Link Functions and Count Data
Ronald Heck Class Notes: Week 8 1 Class Notes: Week 8 Probit versus Logit Link Functions and Count Data This week we ll take up a couple of issues. The first is working with a probit link function. While
Lecture 7 Time-dependent Covariates in Cox Regression
Lecture 7 Time-dependent Covariates in Cox Regression So far, we ve been considering the following Cox PH model: λ(t Z) = λ 0 (t) exp(β Z) = λ 0 (t) exp( β j Z j ) where β j is the parameter for the the
Lecture Notes 12 Advanced Topics Econ 20150, Principles of Statistics Kevin R Foster, CCNY Spring 2012
Lecture Notes 2 Advanced Topics Econ 2050, Principles of Statistics Kevin R Foster, CCNY Spring 202 Endogenous Independent Variables are Invalid Need to have X causing Y not vice-versa or both! NEVER regress
Kausalanalyse. Analysemöglichkeiten von Paneldaten
Kausalanalyse Analysemöglichkeiten von Paneldaten Warum geht es in den folgenden Sitzungen? Sitzung Thema Paneldaten Einführung 1 2 3 4 5 6 7 8 9 10 11 12 13 14 09.04.2008 16.04.2008 23.04.2008 30.04.2008
What s New in Econometrics. Lecture 1
What s New in Econometrics Lecture 1 Estimation of Average Treatment Effects Under Unconfoundedness Guido Imbens NBER Summer Institute, 2007 Outline 1. Introduction 2. Potential Outcomes 3. Estimands and
Gov 2002: 4. Observational Studies and Confounding
Gov 2002: 4. Observational Studies and Confounding Matthew Blackwell September 10, 2015 Where are we? Where are we going? Last two weeks: randomized experiments. From here on: observational studies. What
Item Response Theory for Conjoint Survey Experiments
Item Response Theory for Conjoint Survey Experiments Devin Caughey Hiroto Katsumata Teppei Yamamoto Massachusetts Institute of Technology PolMeth XXXV @ Brigham Young University July 21, 2018 Conjoint
Causality II: How does causal inference fit into public health and what it is the role of statistics?
Causality II: How does causal inference fit into public health and what it is the role of statistics? Statistics for Psychosocial Research II November 13, 2006 1 Outline Potential Outcomes / Counterfactual
Combining multiple observational data sources to estimate causal eects
Department of Statistics, North Carolina State University Combining multiple observational data sources to estimate causal eects Shu Yang* syang24@ncsuedu Joint work with Peng Ding UC Berkeley May 23,
Does city structure cause unemployment?
The World Bank Urban Research Symposium, December 15-17, 2003 Does city structure cause unemployment? The case study of Cape Town Presented by Harris Selod (INRA and CREST, France) Co-authored with Sandrine
Gov 2002: 5. Matching
Gov 2002: 5. Matching Matthew Blackwell October 1, 2015 Where are we? Where are we going? Discussed randomized experiments, started talking about observational data. Last week: no unmeasured confounders
Causal Inference. Prediction and causation are very different. Typical questions are:
Causal Inference Prediction and causation are very different. Typical questions are: Prediction: Predict Y after observing X = x Causation: Predict Y after setting X = x. Causation involves predicting
Assessing Social Vulnerability to Biophysical Hazards. Dr. Jasmine Waddell
Assessing Social Vulnerability to Biophysical Hazards Dr. Jasmine Waddell About the Project Built on a need to understand: The pre-disposition of the populations in the SE to adverse impacts from disaster
Modeling Mediation: Causes, Markers, and Mechanisms
Modeling Mediation: Causes, Markers, and Mechanisms Stephen W. Raudenbush University of Chicago Address at the Society for Resesarch on Educational Effectiveness,Washington, DC, March 3, 2011. Many thanks
Implementing Matching Estimators for. Average Treatment Effects in STATA. Guido W. Imbens - Harvard University Stata User Group Meeting, Boston
Implementing Matching Estimators for Average Treatment Effects in STATA Guido W. Imbens - Harvard University Stata User Group Meeting, Boston July 26th, 2006 General Motivation Estimation of average effect
Selection endogenous dummy ordered probit, and selection endogenous dummy dynamic ordered probit models
Selection endogenous dummy ordered probit, and selection endogenous dummy dynamic ordered probit models Massimiliano Bratti & Alfonso Miranda In many fields of applied work researchers need to model an
The Impacts of the Affordable Care Act on Preventive Services among Racial Groups UNDERGRADUATE RESEARCH THESIS
The Impacts of the Affordable Care Act on Preventive Services among Racial Groups UNDERGRADUATE RESEARCH THESIS Presented in partial fulfillment of the requirements for the Honors Research Distinction
Introduction to Regression Analysis. Dr. Devlina Chatterjee 11 th August, 2017
Introduction to Regression Analysis Dr. Devlina Chatterjee 11 th August, 2017 What is regression analysis? Regression analysis is a statistical technique for studying linear relationships. One dependent
The propensity score with continuous treatments
7 The propensity score with continuous treatments Keisuke Hirano and Guido W. Imbens 1 7.1 Introduction Much of the work on propensity score analysis has focused on the case in which the treatment is binary.