5 papers
Informative inference on the average treatment effect with instrumental variables
Luca Locher, Thomas Richardson, Mats Stensrud
Policy decisions often depend on whether the average treatment effect (ATE) exceeds a specific cutoff. In instrumental variable (IV) settings, point identification of the ATE requi…
Causal Inference: A Tale of Three Frameworks
Linbo Wang, Thomas Richardson, James Robins
Causal inference is a central goal across many scientific disciplines. Over the past several decades, three major frameworks have emerged to formalize causal questions and guide th…
Individual Treatment Effect: Prediction Intervals and Sharp Bounds
Zhehao Zhang, Thomas S. Richardson
Individual treatment effect (ITE) is often regarded as the ideal target of inference in causal analyses and has been the focus of several recent studies. In this paper, we describe…
The Categorical Instrumental Variable Model: Characterization, Partial Identification, and Statistical Inference
Yilin Song, F. Richard Guo, K. C. Gary Chan +1
We study categorical instrumental variable (IV) models with instrument, treatment and outcome taking finitely many values. We derive a simple closed-form characterization of the se…
Bounds on the Distribution of a Sum of Two Random Variables: Revisiting a problem of Kolmogorov with application to Individual Treatment Effects
Zhehao Zhang, Thomas S. Richardson
We revisit the following problem, proposed by Kolmogorov: given prescribed marginal distributions and for random variables respectively, characterize the set of compa…