Correlation does not imply causation. Standard machine learning and deep learning methods are powerful for prediction tasks but inappropriate for answering causal questions like policy impacts or drug treatment effects. In this talk, I provide a brief introduction to causal inference for observational data, present standard statistical/econometric methods for estimating causal effects, introduce recent advancements using machine learning for causal inference, and provide an overview of available Python libraries.
The talk can be broadly organized into three sections: (i) theory, (ii) models, and (iii) packages.
Please refer to the slides for a more in-depth presentation: