Discussion: Can We Get More Out of Experiments?
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1 Discussion: Can We Get More Out of Experiments? Kosuke Imai Princeton University September 4, 2010 Kosuke Imai (Princeton) Discussion APSA 2010 (Washington D.C.) 1 / 9
2 Keele, McConnaughy, and White Question: Can we gain efficiency by adjusting experimental data after the experiment is done? KMW s Answer: Yes, use matching rather than regression 1 Much weaker functional-form assumption 2 Can detect the lack of common support 3 Less data snooping Disadvantages (acknowledged by KMW): 1 May create imbalance in unobservables 2 No design-based variance calculation KMW s proposal: report both unadjusted and adjusted estimates Adjust or not Adjust?: contribution to the important but controversial debate in the literature Kosuke Imai (Princeton) Discussion APSA 2010 (Washington D.C.) 2 / 9
3 Covariate Adjustments in Experiments Pre-randomization adjustments are gold standard Blocking never hurts (Imai, King & Stuart, 2008) Matching can hurt, but in practice it seems to work very well When post-randomization adjustments are desirable? Covariates are unavailable before randomization AND low power Model-based variance calculation: this may be fine but not clear how to compare it with design-based variance Risk of data snooping is always there Which one do you trust if adjusted and unadjusted estimates are different? Some comments about details: 1 Asymptotics: T 0? maybe just refer to Freedman 2 Simulation: Need to account for randomization? 3 Randomization test: broken randomization? 4 Empirical results: unadjusted 0.00(0.822), with replacement 1.25(0.039), without replacement 0.25(0.803) Kosuke Imai (Princeton) Discussion APSA 2010 (Washington D.C.) 3 / 9
4 Another Motivation for Covariate Adjustments Quantities of interest go beyond ATE Heterogenous treatment effects 1 Useful for testing substantive theory 2 Useful for policy-makers Growing methodological literature: 1 Tree-based methods (Imai and Strauss) 2 Generalized additive models (Feller and Holmes) 3 Bayesian Additive Regression Trees (Green and Kern) Key challenge: avoid post-hoc subgroup analysis problem Regularization is required 1 Cross-validation 2 Bayesian prior 3 Penalty function Using treatment effect heterogeneity to generalize experimental results to a larger population Kosuke Imai (Princeton) Discussion APSA 2010 (Washington D.C.) 4 / 9
5 Hartman, Grieve, and Sekhon Disadvantage of randomized experiments: external validity Question: How do we extrapolate from SATT to PATT? HGS s solution: 1 Estimate heterogenous treatment effects via matching 2 Weight pairs to match the population distribution 3 Use placebo tests if possible Application to Pulmonary Artery Catheterization (PAC) Overall, a nice idea with an interesting application Some remaining issues: 1 Variable selection problem: How should one choose variables to include in matching/weighting? 2 Multiple testing problem with placebo tests 3 Variance calculation is no longer randomization-based Suggestion: Use HGS s method with pre-randomization matching Kosuke Imai (Princeton) Discussion APSA 2010 (Washington D.C.) 5 / 9
6 Some Comments about Details Clarifying the identifying assumption: Sample selection based on observables Possibilities of unobserved confounders Bias decomposition: Maybe helpful to decompose them into sample selection bias due to observables and unobservables Should be expressed using potential outcomes, not E(Y i W, T i = 1, I = 1) etc. Variance calculation: Abadie & Imbens standard errors for SATE/SATT What about PATT? Sometimes PATT has smaller standard error than SATT. Additional uncertainty due to sampling from population Kosuke Imai (Princeton) Discussion APSA 2010 (Washington D.C.) 6 / 9
7 Green and Kern Goal: Evaluate the performance of several competing estimators for generalizing SATE to PATE using Monte Carlo simulations Six methods 1 Difference-in-means 2 Linear regression with step-wise variable selection 3 Inverse probability weighting (IPW) 4 Genetic matching with maximum entropy weighting 5 Bayesian Additive Regression Trees (BART) Use of realistic simulation settings based on GSS Linear, nonlinear response surfaces, confounded and unconfounded Findings: 1 The difference-in-means is the worst 2 BART often does better than the others Important contribution given the growing interest in the topic (Stuart et al.; Hartman et al.) Kosuke Imai (Princeton) Discussion APSA 2010 (Washington D.C.) 7 / 9
8 What Does Explain the Findings? No surprise that the diff-in-means performs badly No surprise that linear regression does badly Why does IPW do worse than BART? IPW used here is parametric Stabilized weights could be used Why does MaxEnt do worse then BART? Common support assumption is satisfied No variable selection for MaxEnt? Need for theoretical understanding about the conditions under which each model does and does not work well Report bias and efficiency rather than MSE Kosuke Imai (Princeton) Discussion APSA 2010 (Washington D.C.) 8 / 9
9 Back to the Common Theme Original question: Can we get more out of experiments? Yes, but be careful and use appropriate statistical tools Efficiency gain by pre-treatment covariate adjustments Post-treatment covariate adjustments require a greater care Avoid post-hoc adjustment Variable and model selection issues Variance calculation Going beyond the SATE Heterogenous treatment effects and Extrapolation Avoid post-hoc subgroup analysis problem Variable and model selection Sample selection based on unobservables Experiments vs. observational studies and central role of statistics Internal vs. external validity Small vs. large data sets Kosuke Imai (Princeton) Discussion APSA 2010 (Washington D.C.) 9 / 9
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