Working Paper: NBER ID: w20978
Authors: Christopher A. Hennessy; Ilya A. Strebulaev
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.
Keywords: causal inference; dynamic economies; random assignment; treatment responses; policy evaluation
JEL Codes: C01; C22; C52; C54; G38
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| random assignment (C90) | valid inference (C20) |
| treatment responses (C22) | causal effects (C22) |
| binary policy variables (C25) | attenuation bias (D91) |
| non-binary policy variables (C46) | incorrect signs of treatment responses (C22) |
| absorbing states (H73) | treatment responses equal causal effects (C22) |
| policy transitions (J62) | relationship complexity between treatment responses and causal effects (C32) |