Propensity score matching and subclassification in observational studies with multi‐level treatments

By Shu Yang, Guido W. Imbens, Z. Cui, D. Faries, Z. Kadziola. 2015

138 citations recorded by the source corpus.

In this article, we develop new methods for estimating average treatment effects in observational studies, in settings with more than two treatment levels, assuming unconfoundedness given pretreatment variables. We emphasize propensity score subclassification and matching methods which have been among the most popular methods in the binary treatment literature. Whereas the literature has suggested that these particular propensity‐based methods do not naturally extend to the multi‐level treatment case, we show, using the concept of weak unconfoundedness and the notion of the generalized propensity score, that adjusting for a scalar function of the pretreatment variables removes all biases associated with observed pretreatment variables. We apply the proposed methods to an analysis of the effect of treatments for fibromyalgia. We also carry out a simulation study to assess the finite sample performance of the methods relative to previously proposed methods.

Subfields: Economics and Econometrics, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Statistics and Probability

Citations in the corpus

The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “As shown in Yang et al. [2016], it follows from weak unconfoundedness that”
Propensity Score Weighting for Causal Inference with Clustered Data (2017, methodology): “An extensive literature thereafter proposed a number of estimators based on the propensity score, including matching [3, 4, 5],weighting [6, 7, 8], and stratification [9, 10].”
Econometric Methods for Program Evaluation (2018, background): “…extended to multi-valued treatments (Imbens, 2000; Lechner, 2001; Hirano and Imbens, 2004; Imai and van Dyk, 2004; Cattaneo, 2010; Farrell, 2015; Yang et al., 2016), to quantiles and related treatment effects (Firpo, 2007; Cattaneo, 2010), and to the analysis and interpretation of…”