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About this paper
Michal Ozery-Flato, Pierre Thodoroff, Matan Ninio, Michal Rosen-Zvi, Tal El-Hay
The authors introduce adversarial balancing for causal effect estimation from observational data. The method repeatedly reweights observed samples so a classifier struggles to distinguish a weighted treatment group from a target population; it creates no synthetic people. It outperforms inverse-propensity weighting in most reported tests, though results vary by benchmark and classifier, and still depends on measured confounders and positivity.
Summary by alphaXiv.
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