Estimating heterogeneous causal effects in observational studies using small area predictors

Setareh Ranjbar, Nicola Salvati and Barbara Pacini

Computational Statistics & Data Analysis, 2023, vol. 184, issue C

Abstract: The official statistics produced by National Statistical Institutes are mainly used by policy makers to take decisions. In particular, when policy makers and decision takers would like to know the impact of a given policy, it is important to acknowledge the heterogeneity of the treatment effects for different domains. If the domain of interest is small with regard to its sample size, then the evaluator has entered the small area estimation (SAE) dilemma. Based on the modification of the Inverse Propensity Weighting estimator and the traditional small area predictors, new estimators of area specific average treatment effects are proposed for unplanned domains. A robustified version of the predictor against presence of the outliers is also developed. Analytical Mean Squared Error (MSE) estimators of the proposed predictors are derived. These methods provide a tool to map the policy impacts that can help to better target the treatment group(s). The properties of these small area estimators are illustrated by means of a design-based simulation using a real data set where the aim is to study the effects of permanent versus temporary contracts on the economic insecurity of households in different regions of Italy.

Keywords: M-quantile regression; Linear mixed models; Potential outcome; Inverse propensity scores; Heterogeneity of effects (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:184:y:2023:i:c:s0167947323000531

DOI: 10.1016/j.csda.2023.107742

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