Estimation and Inference for Policy Relevant Treatment Effects
The policy relevant treatment effect (PRTE) measures the average effect of switching from a status-quo policy to a counterfactual policy. Estimation of the PRTE involves estimation of multiple preliminary parameters, including propensity scores, conditional expectation functions of the outcome and covariates given the propensity score, and marginal treatment effects. These preliminary estimators can affect the asymptotic distribution of the PRTE estimator in complicated and intractable manners. In this light, we propose an orthogonal score for double debiased estimation of the PRTE, whereby the asymptotic distribution of the PRTE estimator is obtained without any influence of preliminary parameter estimators as far as they satisfy mild requirements of convergence rates. To our knowledge, this paper is the first to develop limit distribution theories for inference about the PRTE.
Code (0)
등록된 구현이 없습니다.
Tasks
counterfactualSimilar Papers 제목 키워드 기반
Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization
We propose a method for estimation and inference for bounds for heterogeneous causal effect parameters in general sample selection models where the treatment can affect whether an outcome is observed and no exclusion res…
validTreatment Effects in Market Equilibrium
Policy-relevant treatment effect estimation in a marketplace setting requires taking into account both the direct benefit of the treatment and any spillovers induced by changes to the market equilibrium. The standard way…
Experimental DesignEstimation and Inference of Average Treatment Effect in Percentage Points under Heterogeneity
In semi-log regression models with heterogeneous treatment effects, the average treatment effect (ATE) in log points and its exponential transformation minus one underestimate the ATE in percentage points. I propose new …
Forecasted Treatment Effects
We consider estimation and inference of the effects of a policy in the absence of a control group. We obtain unbiased estimators of individual (heterogeneous) treatment effects and a consistent and asymptotically normal …
Time SeriesEstimation of Treatment Effects in Extreme and Unobserved Data
Causal effect estimation seeks to determine the impact of an intervention from observational data. However, the existing causal inference literature primarily addresses treatment effects on frequently occurring events. B…
Causal Inference