Signaling, Random Assignment, and Causal Effect Estimation

22 Pages Posted: 10 Mar 2020

See all articles by Christopher Hennessy

Christopher Hennessy

London Business School

Gilles Chemla

Imperial College Business School; CNRS ; Centre for Economic Policy Research (CEPR)

Date Written: February 18, 2020

Abstract

Causal estimates from randomization are commonly viewed as ideal. However, their primacy for many real-world decisions is questionable. This is because random assignment, in eliminating self-selection, also eliminates signaling. However, outside experiments, agents make discretionary decisions, generating signaling content which alters beliefs, payoffs, and causal effects. Therefore, if the objective is informing optimal discretionary decisions, rather than predicting outcomes under forced actions or noisy mistakes, random assignment can be misleading. In applications from finance, labor, and macroeconomics, we show signaling can amplify, attenuate, or reverse the sign of causal effects derived from random assignment, with corresponding implications for optimal actions.

Keywords: signal, random assignment, causal effect, selection

JEL Classification: D82, E6, G3, J24

Suggested Citation

Hennessy, Christopher and Chemla, Gilles, Signaling, Random Assignment, and Causal Effect Estimation (February 18, 2020). Available at SSRN: https://ssrn.com/abstract=3540327 or http://dx.doi.org/10.2139/ssrn.3540327

Christopher Hennessy

London Business School

Sussex Place
Regent's Park
London, London NW1 4SA
United Kingdom

Gilles Chemla (Contact Author)

Imperial College Business School ( email )

South Kensington Campus
London SW7 2AZ, SW7 2AZ
United Kingdom
+44 207 594 9161 (Phone)
+44 207 594 9210 (Fax)

CNRS ( email )

Dauphine Recherches en Management
Place du Marechal de Lattre de Tassigny
Paris, 75016
France
331 44054970 (Phone)

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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