Beyond Random Assignment: Credible Inference of Causal Effects in Dynamic Economies

47 Pages Posted: 2 Mar 2015

See all articles by Chris Hennessy

Chris Hennessy

London Business School

Ilya A. Strebulaev

Stanford University - Graduate School of Business; National Bureau of Economic Research

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Date Written: February 2015

Abstract

Random assignment is insufficient for measured treatment responses to recover causal effects (comparative statics) in dynamic economies. We characterize analytically bias probabilities and magnitudes. If the policy variable is binary there is attenuation bias. With more than two policy states, treatment responses can undershoot, overshoot, or have incorrect signs. Under permanent random assignment, treatment responses overshoot (have incorrect signs) for realized changes opposite in sign to (small relative to) expected changes. We derive necessary and sufficient conditions, beyond random assignment, for correct inference of causal effects: martingale policy variable. Infinitesimal transition rates are only sufficient absent fixed costs. Stochastic monotonicity is sufficient for correct sign inference. If these conditions are not met, we show how treatment responses can nevertheless be corrected and mapped to causal effects or extrapolated to forecast responses to future policy changes within or across policy generating processes.

Suggested Citation

Hennessy, Christopher and Strebulaev, Ilya A., Beyond Random Assignment: Credible Inference of Causal Effects in Dynamic Economies (February 2015). NBER Working Paper No. w20978. Available at SSRN: https://ssrn.com/abstract=2572137

Christopher Hennessy (Contact Author)

London Business School ( email )

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Ilya A. Strebulaev

Stanford University - Graduate School of Business ( email )

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HOME PAGE: http://faculty-gsb.stanford.edu/strebulaev/

National Bureau of Economic Research ( email )

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