Arthur Charpentier, Emmanuel Flachaire, Ewen Gallic
arXiv 18 Jan 2023 · Econometrics · 4 citations (OpenAlex)
arXiv:2301.07755 · PDF · DOI · OpenAlex · Extracted main text
Many problems ask a question that can be formulated as a causal question: "what would have happened if...?" For example, "would the person have had surgery if he or she had been Black?" To address this kind of questions, calculating an average treatment effect (ATE) is often uninformative, because one would like to know how much impact a variable (such as skin color) has on a specific individual, characterized by certain covariates. Trying to calculate a conditional ATE (CATE) seems more appropriate. In causal inference, the propensity score approach assumes that the treatment is influenced by x, a collection of covariates. Here, we will have the dual view: doing an intervention, or changing the treatment (even just hypothetically, in a thought experiment, for example by asking what would have happened if a person had been Black) can have an impact on the values of x. We will see here that optimal transport allows us to change certain characteristics that are influenced by the variable we are trying to quantify the effect of. We propose here a mutatis mutandis version of the CATE, which will be done simply in dimension one by saying that the CATE must be computed relative to a level of probability, associated to the proportion of x (a single covariate) in the control population, and by looking for the equivalent quantile in the test population. In higher dimension, it will be necessary to go through transport, and an application will be proposed on the impact of some variables on the probability of having an unnatural birth (the fact that the mother smokes, or that the mother is Black).
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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 | Pearl, J. and Mackenzie, D (2018) The book of why: the new science of cause and effect\/ | 0.737 | 3 | 2 | 100% |
| 2 | Kantorovich, L. V (1942) On the translocation of masses | 0.644 | 2 | 2 | 100% |
| 3 | Hernández-Dáz, S., Schisterman, E. F. and Hernán, M. A (2006) The birth weight “paradox” uncovered? American journal of epidemiology\/ 164: 1115–1120, 10.1093/aje/kwj275 | 0.585 | 3 | 1 | 100% |
| 4 | Brualdi, R. A (2006) Combinatorial matrix classes\/, 13\/ | 0.511 | 2 | 1 | 100% |
| 5 | Galichon, A (2016) Optimal transport methods in economics\/ | 0.511 | 2 | 1 | 100% |
| 6 | Villani, C (2003) Topics in optimal transportation\/, 58\/ | 0.511 | 2 | 1 | 100% |
| 7 | Villani, C (2009) Optimal transport: old and new\/, 338\/ | 0.511 | 2 | 1 | 100% |
| 8 | Abrevaya, J., Hsu, Y.-C. and Lieli, R. P (2015) Estimating conditional average treatment effects | 0.405 | 1 | 1 | 100% |
| 9 | Athey, S. and Wager, S (2019) Estimating treatment effects with causal forests: An application | 0.405 | 1 | 1 | 100% |
| 10 | Athey, S., Tibshirani, J. and Wager, S (2019) Generalized random forests | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 44 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | An econometrician's guide to optimal transport | 0.405 | 1 | 1 |