Causal Inference in Cross-Sectional Data, Survival-Time Data, and Panel Data Using Stata


This workshop provides an introduction to causal inference for cross-sectional data, survival-time data, and panel data using Stata. It uses a combination of intuition, mathematics, and computational examples to illustrate what causal inference parameters measure and how we estimate them using Stata.

The workshop begins by explaining the potential-outcome framework that is used to define measures of treatment effects known as the average treatment effect (ATE) and the average treatment effect on the treated (ATET). The workshop continues by looking at different estimators for these effects under different assumptions and shows how to estimate them using Stata. The workshop then explores the meaning and estimation of other treatment-effect measures known as quantile-treatment effects, marginal risk ratios and marginal hazard ratios. In addition to being of useful, these other measures highlight the tricks of causal inference. After reviewing the standard estimators for linear and nonlinear panel data models, the workshop shows how to estimate the ATE and the ATET using these linear and nonlinear panel data models. Morning sessions will introduce methods in a lecture format and afternoon sessions will be hands-on computer sessions.

Prerequisites: You must be familiar and comfortable with the mathematical material assumed in an advanced undergraduate or masters-level econometrics class such as the material in appendices A to E in Introductory Econometrics 5th edition by Jeffrey Wooldridge.

Note: Before enrolling, potential participants in this workshop should review the preliminary workshop syllabus which will be added to the lower section of this page in the near future.

Fee: Members = $1500; Non-members = $3000

Tags: Causal Inference, Cross-Sectional Data, Survival Data, Panel Data, Stata

Course Sections