https://kobybibas.github.io/tags/matching/Recent content in Matching on Koby BibasHugo -- gohugo.ioenSun, 25 Oct 2026 00:00:00 +0000https://kobybibas.github.io/posts/20261025_treatment_effect_observational_data/summary/Sun, 25 Oct 2026 00:00:00 +0000https://kobybibas.github.io/posts/20261025_treatment_effect_observational_data/summary/Causal inference is essential for informed decision-making, as it uncovers the true data-generating processes beyond mere associations found in predictive models. The fundamental challenge in causal effect estimation is that we see the outcome under the treatment a sample received. We do not see the outcome that sample would have had under the other treatment. Short recap from \[1/2\] Estimating a Treatment Effect with Causal Inference: Conditional average treatment effect (CATE) \(\tau(x) = \mathbb{E}[Y(1) - Y(0) \mid x]\).