Consistent tests for conditional treatment effects

Hsu, Yu‐Chin

doi:10.1111/ectj.12077

Cited by 30 publications

(24 citation statements)

References 53 publications

(96 reference statements)

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“…When the two functions being compared are densities, it is a problem for the local two sample test (Duong et al, 2009;Duong, 2013). When the two functions being compared are regression functions, the comparison is related to the conditional average treatment effect curve (Lee and Whang, 2009;Hsu, 2013;Ma and Zhou, 2014;Abrevaya et al, 2015). In the local two sample test, we want to know if two samples are from the same population or not and find out the regions where the two densities differ.…”

Section: Regression Now We Show That Bootstrapping the Debiased Locamentioning

confidence: 99%

Nonparametric inference via bootstrapping the debiased estimator

Cheng¹,

Chen²

2019

Electron. J. Statist.

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In this paper, we propose to construct confidence bands by bootstrapping the debiased kernel density estimator (for density estimation) and the debiased local polynomial regression estimator (for regression analysis). The idea of using a debiased estimator was first introduced in Calonico et al. (2015), where they construct a confidence interval of the density function (and regression function) at a given point by explicitly estimating stochastic variations. We extend their ideas and propose a bootstrap approach for constructing confidence bands that is uniform for every point in the support. We prove that the resulting bootstrap confidence band is asymptotically valid and is compatible with most tuning parameter selection approaches, such as the rule of thumb and cross-validation. We further generalize our method to confidence sets of density level sets and inverse regression problems. Simulation studies confirm the validity of the proposed confidence bands/sets.

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Section: Regression Now We Show That Bootstrapping the Debiased Locamentioning

confidence: 99%

Nonparametric inference via bootstrapping the debiased estimator

Cheng¹,

Chen²

2019

Electron. J. Statist.

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“…For instance, one may consider tests conditional stochastic dominance, or more generally, tests based on conditional moment inequalities, see e.g. Delgado and Escanciano (2013), Chang et al (2015) and Hsu (2017). Given that the procedures considered by Chang et al (2015) and…”

Section: Resultsmentioning

confidence: 99%

Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes

Sant’Anna

2020

Journal of Business & Economic Statistics

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This article proposes different tests for treatment effect heterogeneity when the outcome of interest, typically a duration variable, may be right-censored. The proposed tests study whether a policy 1) has zero distributional (average) effect for all subpopulations defined by covariate values, and 2) has homogeneous average effect across different subpopulations. The proposed tests are based on two-step Kaplan-Meier integrals, and do not rely on parametric distributional assumptions, shape restrictions, nor on restricting the potential treatment effect heterogeneity across different subpopulations. Our framework is suitable not only to exogenous treatment allocation, but can also account for treatment noncompliance, an important feature in many applications. The proposed tests are consistent against fixed alternatives, and can detect nonparametric alternatives converging to the null at the parametric n −1/2 -rate, n being the sample size. Critical values are computed with the assistance of a multiplier bootstrap. The finite sample properties of the proposed tests are examined by means of a Monte Carlo study, and an application about the effect of labor market programs on unemployment duration. Open-source software is available for implementing all proposed tests.

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“…where the second equality holds by a second order mean valued expansion around p(X i ) and p(M i , X i ) and the fact that sup x∈X |p( Hsu (2016) and the conditions given in this paper are sufficient for sup x∈X |p(x) − p(x)| = o p (n −1/4 ). By the same argument as in Theorem 1 of HIR, we have…”

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confidence: 87%

Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting

Hsu

Huber

Lai

2018

Journal of Econometric Methods

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Using a sequential conditional independence assumption, this paper discusses fully nonparametric estimation of natural direct and indirect causal effects in causal mediation analysis based on inverse probability weighting. We propose estimators of the average indirect effect of a binary treatment, which operates through intermediate variables (or mediators) on the causal path between the treatment and the outcome, as well as the unmediated direct effect. In a first step, treatment propensity scores given the mediator and observed covariates or given covariates alone are estimated by nonparametric series logit estimation. In a second step, they are used to reweigh observations in order to estimate the effects of interest. We establish root-n consistency and asymptotic normality of this approach as well as a weighted version thereof. The latter allows evaluating effects on specific subgroups like the treated, for which we derive the asymptotic properties under estimated propensity scores. We also provide a simulation study and an application to an information intervention about male circumcisions.

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Consistent tests for conditional treatment effects

Cited by 30 publications

References 53 publications

Nonparametric inference via bootstrapping the debiased estimator

Nonparametric inference via bootstrapping the debiased estimator

Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes

Nonparametric estimation of natural direct and indirect effects based on inverse probability weighting

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