Causal Inference Methods: Lessons from Applied Microeconomics

76 Pages Posted: 15 Nov 2018

See all articles by Laura Dague

Laura Dague

Texas A&M University - George Bush School of Government and Public Service; National Bureau of Economic Research (NBER)

Joanna Lahey

Texas A&M University - George Bush School of Government and Public Service; National Bureau of Economic Research (NBER)

Date Written: September 19, 2018

Abstract

This paper discusses causal inference techniques for social scientists through the lens of applied microeconomics. We frame causal inference using the standard of the ideal experiment, emphasizing problems of omitted variable bias and reverse causality. We explore how laboratory and field experiments can succeed and fail to meet this ideal in practice. We then outline how different methods and the statistical assumptions behind them can lead to causal inference in non-experimental contexts. We explain when problems with omitted variables bias can and cannot be addressed using observed controls. We consider tools for studying natural experiments, including difference-in-differences, instrumental variables, and regression discontinuity techniques. Finally, we discuss additional concerns that may arise such as weighting, clustering, multiple inference, and external validity. We include Stata code for implementing each of these methods as well as a series of checklists for researchers detailing important robustness and design checks. Throughout, we emphasize the importance of understanding the context of a study and implementing analyses in a way that acknowledges strengths and limitations.

Keywords: microeconomics, experiments, difference-in-differences, instrumental variables, regression discontinuitity, practical methods, analytic methods, stata code, checklists

JEL Classification: C01, C12, C18, C36, C8, C9, D00

Suggested Citation

Dague, Laura and Lahey, Joanna, Causal Inference Methods: Lessons from Applied Microeconomics (September 19, 2018). Available at SSRN: https://ssrn.com/abstract=3279782 or http://dx.doi.org/10.2139/ssrn.3279782

Laura Dague

Texas A&M University - George Bush School of Government and Public Service ( email )

4220 TAMU
College Station, TX 77843
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Joanna Lahey (Contact Author)

Texas A&M University - George Bush School of Government and Public Service ( email )

TAMU 4220
1004 George Bush Dr West
College Station, TX 77843
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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