Causal Inference with Machine Learning - EXPLAINED!

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REFERENCES
[1] RCTs may not model ATE exactly as we think. But more importantly, they don’t measure ITEs: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6019115/
[2] Literature Review of Causal Inference + Uplift modeling: https://proceedings.mlr.press/v67/gutierrez17a/gutierrez17a.pdf
[3] Quick intro to uplift modeling: https://towardsdatascience.com/a-quick-uplift-modeling-introduction-6e14de32bfe0
[4] Why Uplift modeling in marketing is important: https://towardsdatascience.com/why-every-marketer-should-consider-uplift-modeling-1090235572ec
[5] Uplift Modeling: https://link.springer.com/content/pdf/10.1007/s10115-011-0434-0.pdf
[6] Code for causalml: https://github.com/uber/causalml
[7] Section 3 here shows the assumptions that need to be met for an RCT to give an estimate of ATE that is representative of the population: https://rss.onlinelibrary.wiley.com/doi/10.1111/rssa.12094
[8] Causal ML documentation about methodologies to determine CATE: https://causalml.readthedocs.io/en/latest/methodology.html#meta-learner-algorithms
[9] MIT lecture on covariate adjustment & matching: https://www.youtube.com/watch?v=g5v-NvNoJQQ
[10] Microsoft’s Blog post illustrating different methods to determine CATE: https://medium.com/data-science-at-microsoft/causal-inference-part-2-of-3-selecting-algorithms-a966f8228a2d
[11] Wayfair Tech blog that succinctly explains Uplift Decision Trees (I’ll probably make a video on this in the future): https://www.aboutwayfair.com/tech-innovation/modeling-uplift-directly-uplift-decision-tree-with-kl-divergence-and-euclidean-distance-as-splitting-criteria
[12] Article that ties the research paper with the meta-learner algorithms: https://chowdera.com/2021/10/20211025140618524t.html#2_upliftMetalearning_methods_31

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