
Health Economist @UCBerkeley Public Health. Studying digital health - telemedicine, AI. IV hobbyist. Ex-Tel Aviv U Econ. Princeton Econ PhD
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ALT Screenshot of the paper's title page from the American Economic Review (2026, Vol. 116, No. 4). Title: "Negative Control Falsification Tests for Instrumental Variable Designs" by Oren Danieli, Daniel Nevo, Itai Walk, Bar Weinstein, and Dan Zeltzer. The abstract states that IV designs are typically tested using two types of falsification tests, characterized as conditional independence tests between negative control variables and the IV or outcome. The paper describes conditions these proxies must satisfy, shows these tests examine not only independence and the exclusion restriction but also functional form assumptions, and reveals that conventional applications may flag problems even in valid IV designs.

ALT Appendix Figure 1: A four-step checklist for implementing negative control tests. Step 1 — Articulate potential threats to IV design: characterize threats as alternative paths through an unobserved variable U, distinguishing Alternative Path Outcome (APO) and Alternative Path Instrument (API) types. Step 2 — Survey available data to identify negative control variables: NCOs proxy for APO variables, NCIs proxy for API variables. Step 3 — Select negative control test specification: NCO tests assess independence between IV and NCO variables; NCI tests evaluate independence between NCI variables and the outcome conditional on the IV. Step 4 — Interpret results: if the null is rejected, investigate IV design vs. functional form violations and use multiple NCs to diagnose specific threats; if not rejected, consider insufficient power or untested alternative paths.





















