Estimation of a score-explained non-randomized treatment effect in fixed and high dimensions

TL;DR

This paper introduces SCENTS, a new method for estimating non-randomized treatment effects at a parametric rate, even with confounding and high-dimensional data, improving over existing slower convergence methods.

Contribution

The paper proposes a novel SCENTS model and estimator that achieve $ oot n$ convergence rates for treatment effect estimation in confounded and high-dimensional settings.

Findings

Estimator is asymptotically normal and semi-parametrically efficient under normal errors.

Method extends to high-dimensional covariates with a de-biasing procedure.

Application to real datasets demonstrates improved performance over previous methods.

Abstract

Non-randomized treatment effect models are widely used for the assessment of treatment effects in various fields and in particular social science disciplines like political science, psychometry, psychology. More specifically, these are situations where treatment is assigned to an individual based on some of their characteristics (e.g. scholarship is allocated based on merit or antihypertensive treatments are allocated based on blood pressure level) instead of being allocated randomly, as is the case, for example, in randomized clinical trials. Popular methods that have been largely employed till date for estimation of such treatment effects suffer from slow rates of convergence (i.e. slower than $\sqrt{n}$). In this paper, we present a new model coined SCENTS: Score Explained Non-Randomized Treatment Systems, and a corresponding method that allows estimation of the treatment effect at $\sqrt{n}$ rate in the presence of fairly general forms of confoundedness, when the `score' variable on whose basis treatment is assigned can be explained via certain feature measurements of the individuals under study. We show that our estimator is asymptotically normal in general and semi-parametrically efficient under normal errors. We further extend our analysis to high dimensional covariates and propose a $\sqrt n$ consistent and asymptotically normal estimator based on a de-biasing procedure. Our analysis for the high dimensional incarnation can be readily extended to analyze partial linear models in the presence of noisy variables corresponding to the non-linear part of the model, where the noise can be correlated with the variables corresponding to the linear part. We analyze two real datasets via our method and compare our results with those obtained by using previous approaches. We conclude this paper with a discussion on some possible extensions of our approach.