Regression Discontinuity Designs Using Covariates

We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions, and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. An empirical illustration and an extensive simulation study is presented. All methods are implemented in \texttt{R} and \texttt{Stata} software packages

Housing, Health and Happiness

Despite the importance of housing for people’s well-being, there is little evidence on the causal impact of housing and housing improvement programs on health and welfare. In this paper, we help to fill this gap by investigating the impact of a large-scale effort by the Mexican Government to replace dirt floors with cement floors on child health and adult happiness. We find that replacing dirt floors with cement floors significantly improves the health of young children. Specifically, we find significant decreases in the incidence of parasitic infestations, diarrhea, and the prevalence of anemia, and an improvement in children’s cognitive development. Additionally, we find that replacing dirt floors by cement floors significantly improves adult welfare, as measured by increased satisfaction with their housing and quality of life, as well as by lower scores on depression and perceived stress scales.

A Guide to Regression Discontinuity Designs in Medical Applications

We present a practical guide for the analysis of regression discontinuity (RD) designs in biomedical contexts. We begin by introducing key concepts, assumptions, and estimands within both the continuity-based framework and the local randomization framework. We then discuss modern estimation and inference methods within both frameworks, including approaches for bandwidth or local neighborhood selection, optimal treatment effect point estimation, and robust bias-corrected inference methods for uncertainty quantification. We also overview empirical falsification tests that can be used to support key assumptions. Our discussion focuses on two particular features that are relevant in biomedical research: (i) fuzzy RD designs, which often arise when therapeutic treatments are based on clinical guidelines, but patients with scores near the cutoff are treated contrary to the assignment rule; and (ii) RD designs with discrete scores, which are ubiquitous in biomedical applications. We illustrate our discussion with three empirical applications: the effect CD4 guidelines for anti-retroviral therapy on retention of HIV patients in South Africa, the effect of genetic guidelines for chemotherapy on breast cancer recurrence in the United States, and the effects of age-based patient cost-sharing on healthcare utilization in Taiwan. Complete replication materials employing publicly available statistical software in Python, R and Stata are provided, offering researchers all necessary tools to conduct an RD analysis

A Practical Introduction to Regression Discontinuity Designs: Extensions

This monograph, together with its accompanying first part Cattaneo, Idrobo and Titiunik (2020), collects and expands the instructional materials we prepared for more than $40$ short courses and workshops on Regression Discontinuity (RD) methodology that we taught between 2014 and 2022. In this second monograph, we discuss several topics in RD methodology that build on and extend the analysis of RD designs introduced in Cattaneo, Idrobo and Titiunik (2020). Our first goal is to present an alternative RD conceptual framework based on local randomization ideas. This methodological approach can be useful in RD designs with discretely-valued scores, and can also be used more broadly as a complement to the continuity-based approach in other settings. Then, employing both continuity-based and local randomization approaches, we extend the canonical Sharp RD design in multiple directions: fuzzy RD designs, RD designs with discrete scores, and multi-dimensional RD designs. The goal of our two-part monograph is purposely practical and hence we focus on the empirical analysis of RD designs

Housing, health, and happiness

Despite the importance of housing for people's well-being, there has been little work done to assess the causal impact of housing and housing improvement programs on health and welfare. In this paper the authors help fill this gap by investigating the impact of a large-scale effort by the Mexican government to replace dirt floors with cement floors on child health and adult happiness. They find that replacing dirt floors with cement floors significantly reduces parasitic infestations in young children, reduces diarrhea, reduces anemia, and improves cognitive development. Finally, they also find that this program leave adults substantially better off, as measured by satisfaction with their housing and quality of life and by their significantly lower rates of depression and perceived stress.Health Monitoring&Evaluation,Disease Control&Prevention,Housing&Human Habitats,Access to Finance,Construction Industry

The Regression Discontinuity Design

This handbook chapter gives an introduction to the sharp regression discontinuity design, covering identification, estimation, inference, and falsification methods