QUANTITATIVE RESEARCH METHODS WORKSHOP
Abstract: Estimating the direct effect of a treatment fixing the value of a consequence of that treatment is becoming a common part of social science research. In many cases, however, these effects are difficult to estimate standard methods since they can induce post-treatment bias. More complicated methods like marginal structural models or structural nested mean models can recover direct effects in these situations but require parametric models for the outcome or the post-treatment covariates. In this paper, we propose an alternative approach, which we call \emph{telescope matching}, to estimating direct effects. The method combines matching and regression to impute missing counterfactual outcomes in a flexible manner. Using simulation and empirical studies, we show how this approach weakens model dependence for researchers estimating direct treatment effects.
Matthew Blackwell is an Associate Professor of Government at Harvard University and an affiliate of the Institute for Quantitative Social Science. He studies political methodology, with a focus on dynamic causal inference, instrumental variables, experimental design, missing data, and panel data. He is the co-author of the book Deep Roots: How Slavery Still Shapes Southern Politics published by Princeton University Press.
The Quantitative Research Methods Workshop series is being sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund. Lunch will be served.