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Causal Inference for Asset Pricing
(with Valentin Haddad, Zhiguo He, Paul Huebner, and Peter Kondor)

First version: Fall 2024
This version:  Summer 2025


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Abstract:

This paper provides a guide for using causal inference with asset prices and quantities.
Our framework revolves around an elementary assumption about portfolio demand: ho-
mogeneous substitution conditional on observables. Under this assumption, standard
cross-sectional instrumental variables or difference-in-difference regressions identify the
relative demand elasticity between assets with the same observables, the difference be-
tween own-price and cross-price elasticity. In contrast, identifying aggregate elasticities
and substitution along specific characteristics requires joint estimation using multiple
sources of exogenous time-series variation. The same principles apply to the estimation
of multipliers measuring the price impact of supply or demand shocks. Our assumption
maps to familiar restrictions on covariance matrices in classical asset pricing models,
encompass demand models such as logit, and accommodate rich substitution patterns
even outside of these models. We discuss how to design experiments satisfying this
condition and offer diagnostics to validate it.


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