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Causal AI, Effect Heterogeneity & Understanding ML || Alicia Curth || Causal Bandits Ep. 006 (2023)
Causal Bandits Podcast
Navigating Treatment Effect Estimation
In treatment effect estimation, two key challenges arise. First, covariate shift occurs when the characteristics of treated and untreated groups differ significantly, complicating model fitting and analysis. Second, the desired label representing an individual's potential outcomes under treatment and non-treatment is often unobserved, posing difficulties in learning and insight extraction. These complexities distinguish treatment effect estimation from standard prediction problems, making the process both challenging and intriguing.
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