This paper presents a method of calculating sharp bounds on the average treatment effect using linear programming under identifying assumptions commonly used in the literature. This new method provides a sensitivity analysis of the identifying assumptions and missing data in an application...

Causality for Risk Analysts: Improving Our Understanding of How the World Works -- Improving Individual Risk Management Decisions: Learning from Experience and Coping with Model Uncertainty -- Improving Community Risk Management: Managing Disaster Risks -- Improving Organizational Risk...

A researcher is interested in a set of variables that he wants to model with a vector auto-regression and he has a dataset with more variables. Which variables from the dataset to include in the VAR, in addition to the variables of interest? This question arises in many applications of VARs, in...

This paper explores the implications of possible bias cancellation using Rubin-style matching methods with complete and incomplete data. After reviewing the na?ve causal estimator and the approaches of Heckman and Rubin to the causal estimation problem, we show how missing data can complicate...