Federico A. Bugni, Ivan A. Canay, Steve McBride
arXiv 22 Feb 2023 · Econometrics · publishedJournal of Econometrics (2025) · 1 citations (OpenAlex)
arXiv:2302.11505 · PDF · DOI · OpenAlex · Extracted main text
This paper studies settings where the analyst is interested in identifying and estimating the average direct causal effect of a binary treatment on an outcome. We consider a setup in which the outcome realization does not get immediately realized after the treatment assignment, a feature that is ubiquitous in empirical settings. The period between the treatment and the realization of the outcome allows other observed actions to occur and affect the outcome. In this context, we study several regression-based estimands routinely used in empirical work to capture the average treatment effect and shed light on interpreting them in terms of ceteris paribus effects, indirect causal effects, and selection terms. We obtain three main and related takeaways under a common set of assumptions. First, the three most popular estimands do not generally satisfy what we call strong sign preservation, in the sense that these estimands may be negative even when the treatment positively affects the outcome conditional on any possible combination of other actions. Second, the most popular regression that includes the other actions as controls satisfies strong sign preservation if and only if these actions are mutually exclusive binary variables. Finally, we show that a linear regression that fully stratifies the other actions leads to estimands that satisfy strong sign preservation.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Imai, K., Keele, L. and Yamamoto, T (2010) Identification, inference and sensitivity analysis for causal mediation effects | 1.000 | 9 | 3 | 100% |
| 2 | Beaman, L., Karlan, D., Thuysbaert, B. and Udry, C (2013) Profitability of Fertilizer: Experimental Evidence from Female Rice Farmers in Mali | 1.000 | 5 | 4 | 100% |
| 3 | Baron, R. M. and Kenny, D. A (1986) The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerati… | 1.000 | 5 | 3 | 100% |
| 4 | Pearl, J (2001) Direct and indirect effects | 1.000 | 5 | 3 | 100% |
| 5 | Robins, J. M (2003) Semantics of causal dag models and the identification of direct and indirect effects | 1.000 | 5 | 3 | 100% |
| 6 | VanderWeele, T. J., Vansteelandt, S. and Robins, J. M (2014) Effect decomposition in the presence of an exposure-induced mediator–outcome confounder | 0.928 | 4 | 3 | 100% |
| 7 | Manski, C. F (1997) Monotone Treatment Response | 0.843 | 3 | 3 | 100% |
| 8 | Akhtari, M., Chen, J., Lemionet, A., Nguyen, D., Obeid, H. and Zhu, Y (2021) How Airbnb measures future value to standardize tradeoffs | 0.843 | 3 | 3 | 100% |
| 9 | Heckman, J. J (2000) Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective* | 0.843 | 3 | 3 | 100% |
| 10 | Kaufman, J. S (2009) Commentary: Gilding the black box | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 47 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Identifying Treatment and Spillover Effects Using Exposure Contrasts | 0.511 | 2 | 2 |
| 2 | Potential weights and implicit causal designs in linear regression | 0.405 | 1 | 1 |