arXiv subjects

Phillip Heiler

Publications and source records attributed to Phillip Heiler.

8 recordsLinked to original sources

arXiv2026

Sharp Bounds and Inference in Sample Selection Models with Treatment Endogeneity

This paper provides partial identification and inference for treatment effects in nonparametric sample selection models with endogenous treatment and (weak) sample selection monotonicity. Outcomes are observed only for a non-randomly selected subsample and treatment is endogenous because of noncompliance with assignment. The proposed bounds for intensive margin treatment effects among compliers are sharp and tighter than those of Chen and Flores (2015). For inference, we develop semiparametrically efficient orthogonal moments and a debiased machine learning procedure that permits valid root-$n$ inference under high-dimensional covariates and/or flexible functional forms. Simulation results indicate good finite sample performance. Applications to Job Corps and the Oregon Health Insurance Experiment show that the method can deliver substantially tighter effect bounds and confidence intervals than existing alternatives.

econ.EM↗

arXiv2025

Heterogeneity Analysis with Heterogeneous Treatments

Analysis of effect heterogeneity at the group level is standard practice in empirical treatment evaluation. However, treatments analyzed are often aggregates of multiple underlying treatments which are themselves heterogeneous, e.g. different modules of training programs. In these settings, conventional approaches such as comparing (adjusted) differences-in-means across groups can produce misleading conclusions when underlying treatment propensities differ systematically between groups. This paper develops a novel decomposition framework that disentangles contributions of effect heterogeneity and distinct components of treatment heterogeneity to observed group-level differences. We propose debiased machine learning estimators that adapt to many discrete and/or continuous treatments and limited overlap. We revisit a widely documented gender gap in training returns of an active labor market policy. The decomposition reveals that it is almost entirely driven by women being treated differently rather than different returns from identical treatments. In particular, women are disproportionately targeted towards vocational training tracks with lower unconditional returns.

econ.EM↗

arXiv2024

Treatment Evaluation at the Intensive and Extensive Margins

This paper provides a solution to the evaluation of treatment effects in selective samples when neither instruments nor parametric assumptions are available. We provide sharp bounds for average treatment effects under a conditional monotonicity assumption for all principal strata, i.e. units characterizing the complete intensive and extensive margins. Most importantly, we allow for a large share of units whose selection is indifferent to treatment, e.g. due to non-compliance. The existence of such a population is crucially tied to the regularity of sharp population bounds and thus conventional asymptotic inference for methods such as Lee bounds can be misleading. This problem can be solved using smoothed outer identification regions for inference. We provide semiparametrically efficient debiased machine learning estimators for both regular and smooth bounds that can accommodate high-dimensional covariates and flexible functional forms. Our study of active labor market policy reveals the empirical prevalence of the aforementioned indifference population and supports results from previous impact analyses under much weaker assumptions.

econ.EM↗

arXiv2023

A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health

This paper is concerned with identification, estimation, and specification testing in causal evaluation problems when data is selective and/or missing. We leverage recent advances in the literature on graphical methods to provide a unifying framework for guiding empirical practice. The approach integrates and connects to prominent identification and testing strategies in the literature on missing data, causal machine learning, panel data analysis, and more. We demonstrate its utility in the context of identification and specification testing in sample selection models and field experiments with attrition. We provide a novel analysis of a large-scale cluster-randomized controlled teacher's aide trial in Danish schools at grade 6. Even with detailed administrative data, the handling of missing data crucially affects broader conclusions about effects on mental health. Results suggest that teaching assistants provide an effective way of improving internalizing behavior for large parts of the student population.

econ.EM↗

arXiv2022

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 restrictions are available. The method provides conditional effect bounds as functions of policy relevant pre-treatment variables. It allows for conducting valid statistical inference on the unidentified conditional effects. We use a flexible debiased/double machine learning approach that can accommodate non-linear functional forms and high-dimensional confounders. Easily verifiable high-level conditions for estimation, misspecification robust confidence intervals, and uniform confidence bands are provided as well. We re-analyze data from a large scale field experiment on Facebook on counter-attitudinal news subscription with attrition. Our method yields substantially tighter effect bounds compared to conventional methods and suggests depolarization effects for younger users.

econ.EM↗

arXiv2021

Effect or Treatment Heterogeneity? Policy Evaluation with Aggregated and Disaggregated Treatments

Binary treatments are often ex-post aggregates of multiple treatments or can be disaggregated into multiple treatment versions. Thus, effects can be heterogeneous due to either effect or treatment heterogeneity. We propose a decomposition method that uncovers masked heterogeneity, avoids spurious discoveries, and evaluates treatment assignment quality. The estimation and inference procedure based on double/debiased machine learning allows for high-dimensional confounding, many treatments and extreme propensity scores. Our applications suggest that heterogeneous effects of smoking on birthweight are partially due to different smoking intensities and that gender gaps in Job Corps effectiveness are largely explained by differential selection into vocational training.

econ.EM↗

arXiv2020

Efficient Covariate Balancing for the Local Average Treatment Effect

This paper develops an empirical balancing approach for the estimation of treatment effects under two-sided noncompliance using a binary conditionally independent instrumental variable. The method weighs both treatment and outcome information with inverse probabilities to produce exact finite sample balance across instrument level groups. It is free of functional form assumptions on the outcome or the treatment selection step. By tailoring the loss function for the instrument propensity scores, the resulting treatment effect estimates exhibit both low bias and a reduced variance in finite samples compared to conventional inverse probability weighting methods. The estimator is automatically weight normalized and has similar bias properties compared to conventional two-stage least squares estimation under constant causal effects for the compliers. We provide conditions for asymptotic normality and semiparametric efficiency and demonstrate how to utilize additional information about the treatment selection step for bias reduction in finite samples. The method can be easily combined with regularization or other statistical learning approaches to deal with a high-dimensional number of observed confounding variables. Monte Carlo simulations suggest that the theoretical advantages translate well to finite samples. The method is illustrated in an empirical example.

econ.EM↗

arXiv2019

Shrinkage for Categorical Regressors

This paper introduces a flexible regularization approach that reduces point estimation risk of group means stemming from e.g. categorical regressors, (quasi-)experimental data or panel data models. The loss function is penalized by adding weighted squared l2-norm differences between group location parameters and informative first-stage estimates. Under quadratic loss, the penalized estimation problem has a simple interpretable closed-form solution that nests methods established in the literature on ridge regression, discretized support smoothing kernels and model averaging methods. We derive risk-optimal penalty parameters and propose a plug-in approach for estimation. The large sample properties are analyzed in an asymptotic local to zero framework by introducing a class of sequences for close and distant systems of locations that is sufficient for describing a large range of data generating processes. We provide the asymptotic distributions of the shrinkage estimators under different penalization schemes. The proposed plug-in estimator uniformly dominates the ordinary least squares in terms of asymptotic risk if the number of groups is larger than three. Monte Carlo simulations reveal robust improvements over standard methods in finite samples. Real data examples of estimating time trends in a panel and a difference-in-differences study illustrate potential applications.

econ.EM↗