Causal Interaction in Factorial Experiments: Application to Conjoint Analysis
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1 Causal Interaction in Factorial Experiments: Application to Conjoint Analysis Naoki Egami Kosuke Imai Princeton University Talk at the Institute for Mathematics and Its Applications University of Minnesota September 15, 2017 Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 1 / 16
2 Causal Heterogeneity and Interaction Effects 1 Moderation: How does the effect of a treatment vary across individuals? Interaction between the treatment variable and pre-treatment covariates 2 Causal interaction: What combination of treatments is efficacious? Interaction among multiple treatment variables Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 2 / 16
3 Conjoint Analysis Survey experiments with a factorial design Respondents evaluate several pairs of randomly selected profiles defined by multiple factors Social scientists use it to analyze multidimensional preferences Example: Immigration preference (Hopkins and Hainmueller 2014) representative sample of 1,407 American adults each respondent evaluates 5 pairs of immigrant profiles gender 2, education 7, origin 10, experience 4, plan 4, language 4, profession 11, application reason 3, prior trips 5 What combinations of immigrant characteristics do Americans prefer? High dimension: over 1 million treatment combinations Methodological challenges: Many interaction effects false positives, difficulty of interpretation Very few applied researchers study interaction Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 3 / 16
4 Factorial Experiments with Two Treatments Two factorial treatments (e.g., gender and race): A A = {a 0, a 1,..., a LA 1} B B = {b 0, b 1,..., b LB 1} Assumption: Full factorial design 1 Randomization of treatment assignment {Y (a l, b m )} al A,b m B {A, B} 2 Non-zero probability for all treatment combination Pr(A = a l, B = b m ) > 0 for all a l A and b m B Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 4 / 16
5 Main Causal Estimands in Factorial Experiments 1 Average Combination Effect (ACE): Average effect of treatment combination (A, B) = (a l, b m ) relative to the baseline condition (A, B) = (a 0, b 0 ) τ AB (a l, b m ; a 0, b 0 ) = E{Y (a l, b m ) Y (a 0, b 0 )} Effect of being Asian male 2 Average Marginal Effect (AME; Hainmueller et al. 2014; Dasgupta et al. 2015): Average effect of treatment A = a l relative to the baseline condition A = a 0 averaging over the other treatment B ψ A (a l, a 0 ) = E{Y (a l, B) Y (a 0, B)}dF (B) Effect of being male averaging over race Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 5 / 16
6 The New Causal Interaction Effect Average Marginal Interaction Effect (AMIE): π AB (a l, b m ; a 0, b 0 ) = τ AB (a l, b m ; a 0, b 0 ) ψ }{{} A (a l, a 0 ) ψ }{{} B (b m, b 0 ) }{{} ACE of (a l, b m) AME of a l AME of b m Interpretation: additional effect induced by A = a l and B = b m together beyond the separate effect of A = a l and that of B = b m Additional effect of being Asian male beyond the sum of separate effects for being male and being Asian Decomposition of ACE: τ AB = ψ A + ψ B + π AB Invariance: Unlike the standard interaction effect, the relative magnitude of AMIE doesn t depend on the choice of baseline condition AMIEs depend on the distribution of treatment assignment: 1 specified by one s experimental design 2 motivated by a target population Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 6 / 16
7 Higher-order Causal Interaction J factorial treatments with L j levels each: T = (T 1,..., T J ) Assumptions: 1 Full factorial design Y (t) T and Pr(T = t) > 0 for all t 2 Independent treatment assignment T j T j for all j Assumption 2 is not necessary for identification but considerably simplifies estimation We are interested in the K-way interaction where K J We extend all the results for the 2-way interaction to this general case Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 7 / 16
8 Higher-order Average Marginal Interaction Effect General definition: the difference between ACE and the sum of all lower-order AMIEs (first-order AMIE = AME) Example: 3-way AMIE, π 1:3 (t 1, t 2, t 3 ; t 01, t 02, t 03 ), equals τ 1:3 (t 1, t 2, t 3 ; t 01, t 02, t 03 ) }{{} ACE { π 1:2 (t 1, t 2 ; t 01, t 02 ) + π 2:3 (t 2, t 3 ; t 02, t 03 ) + π 1:3 (t 1, t 3 ; t 01, t 03 ) } }{{} sum of all 2-way AMIEs { ψ(t 1 ; t 01 ) + ψ(t 2 ; t 02 ) + ψ(t 3 ; t 03 ) } }{{} sum of AMEs Properties: 1 K-way ACE = the sum of all K-way and lower-order AMIEs 2 Invariance to the baseline condition Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 8 / 16
9 Nonparametric Estimation of AMIE 1 Difference-in-means estimator estimate ACE and AMEs using the difference-in-means estimators estimate AMIE as ˆπ AB = ˆτ AB ˆψ A ˆψ B higher-order AMIEs can be estimated sequentially uses the empirical treatment assignment distribution 2 ANOVA based estimator saturated ANOVA include all interactions up to the Jth order weighted zero-sum constraints: for all factors and levels, L A 1 l=0 L B 1 m=0 Pr(A i = a l )β A l = 0, Pr(B i = b m)β B m = 0, L A 1 l=0 L B 1 m=0 AMIEs are differences of coefficients: Pr(A i = a l )β AB lm = 0, Pr(B i = b m)β AB lm = 0, and so on E( ˆβ A l ˆβ A 0 ) = ψ A (a l ; a 0 ), E( ˆβ AB lm ˆβ AB 00 ) = π AB (a l, b m ; a 0, b 0 ) can use any marginal treatment assignment distribution of choice Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 9 / 16
10 Conjoint Analysis of Ethnic Voting in Africa Ethnic voting and accountability: Carlson (2015, World Politics) Do voters prefer candidates of same ethnicity regardless of their prior performance? Do ethnicity and performance interact? Conjoint analysis in Uganda: 547 voters from 32 villages Each voter evaluates 3 pairs of hypothetical candidates 5 factors: Coethnicity 2, Prior record 2, Prior office 4, Platform 3, Education 8 Prior record = No if Prior office = businessman combine these two factors into a single factor with 7 levels Collapse Education into 2 levels: relevant degrees (MA in business, law, economics, development) and other degrees Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 10 / 16
11 A Statistical Model of Preference Differentials ANOVA regression with one-way and two-way effects: Y i (T i ) = µ + L J j 1 β j l 1{T ij = l} + L j 1 j j j=1 l=0 l=0 L j 1 m=0 β jj lm 1{T ij = l, T ij = m} + ɛ i with appropriate weighted zero-sum constraints In conjoint analysis, we observe the sign of preference differentials Linear probability model of preference differential: Pr(Y i (T i ) > Y i (T i ) T i, T i ) L J j 1 = µ + β j l (1{T ij = l} 1{Tij = l}) + j j j=1 L j 1 l=0 l=0 L j 1 m=0 β jj lm (1{T ij = l, T ij = m} 1{Tij = l, T ij = m}) where µ = 0.5 if the position of profile does not matter We apply regularized ANOVA method (Post and Bondell) Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 11 / 16
12 Ranges of Estimated AMEs and AMIEs Selection Range prob. AME Record Coethnicity Platform Degree AMIE Coethnicity Record Record Platform Platform Coethnic Coethnicity Degree Platform Degree Record Degree Factor selection probability based on bootstrap Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 12 / 16
13 Close Look at the Estimated AMEs Selection Factor AME prob. Record Yes/Village Yes/District Yes/MP No/Village No/District No/MP { No/Businessman Platform { Jobs Clinic base base { Education Coethnicity Degree Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 13 / 16
14 Effect of Regularization on AMIEs Yes/Village Yes/District Yes/MP No/Village No/District No/MP No/Business Yes/Village Yes/District Yes/MP No/Village No/District No/MP No/Business Platform Coethnicity Yes/Village Yes/District Yes/MP No/Village No/District No/MP No/Business Record Yes/Village Yes/District Yes/MP No/Village No/District No/MP No/Business Record Without Regularization With Regularization Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 14 / 16
15 Decomposition and Conditional Effects Decomposition of ACE (Coethnicity Record interaction): τ(coethnic, No/Business; Non-coethnic, No/MP) }{{} 2.4 = ψ(coethnic; Non-coethnic) + ψ(no/business; No/MP) }{{}}{{} π(coethnic, No/Business; Non-coethnic, No/MP) }{{} 3.1 Conditional effects (Platform Record interaction): AMIE: π(education, No/MP}; {Job, No/MP}) = 2.3 Conditional effect of Education relative to Job for No/MP is approximately zero AME: ψ(education; Job) = 2.3 Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 15 / 16
16 Concluding Remarks Interaction effects play an essential role in causal heterogeneity 1 moderation 2 causal interaction Randomized experiments with a factorial design 1 useful for testing multiple treatments and their interactions 2 social science applications: audit studies, conjoint analysis 3 challenge: estimation and interpretation in high dimension Average Marginal Interaction Effect (AMIE) 1 invariant to baseline condition 2 straightforward interpretation even for high order interaction 3 enables effect decomposition 4 enables regularization through ANOVA Egami and Imai (Princeton) Causal Interaction UM-IMA (Sept. 15, 2017) 16 / 16
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