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https://matheusfacure.github.io/python-causality-handbook/24-The-Diff-in-Diff-Saga.htmlAfter discussing treatment effect heterogeneity, we will now switch gears a bit, back into average treatment effects. Over the next few chapters, we will cover some recent developments in panel data methods. A panel is a data structure that has repeated observations across time. The fact that we observe the sa..

https://matheusfacure.github.io/python-causality-handbook/22-Debiased-Orthogonal-Machine-Learning.htmlThe next meta-learner we will consider actually came before they were even called meta-learners. As far as I can tell, it came from an awesome 2016 paper that sprung a fruitful field in the causal inference literature. The paper was called Double Machine Learning for Treatment and Causal Paramet..

https://matheusfacure.github.io/python-causality-handbook/21-Meta-Learners.htmlJust to recap, we are now interested in finding treatment effect heterogeneity, that is, identifying how units respond differently to the treatment. In this framework, we want to estimate or, E[δYi(t)|X] in the continuous case. In other words, we want to know how sensitive the units are to the treatment. This is super..

https://matheusfacure.github.io/python-causality-handbook/20-Plug-and-Play-Estimators.htmlSo far, we’ve seen how to debias our data in the case where the treatment is not randomly assigned, which results in confounding bias. That helps us with the identification problem in causal inference. In other words, once the units are exchangeable, or Y(0),Y(1)⊥T|X, it becomes possible to learn the treatm..

https://matheusfacure.github.io/python-causality-handbook/18-Heterogeneous-Treatment-Effects-and-Personalization.htmlFrom Predictions to Causal InferenceIn the last chapter, we briefly covered Machine Learning models. ML models are tools for what I called predictions or, more technically, estimating the conditional expectation function E[Y|X]. In other words, ML is incredibly useful when you wan..

https://arxiv.org/pdf/2305.18793

https://arxiv.org/pdf/2305.18793

https://arxiv.org/pdf/2305.18793