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REFERENCES

[1] MIT lecture on Causal Inference. Great for the basic idea and big picture: https://www.youtube.com/watch?v=gRkUhg9Wb-I
[2] Great 3 part blogpost that delves into more detail by Microsoft: https://medium.com/data-science-at-microsoft/causal-inference-part-1-of-3-understanding-the-fundamentals-816f4723e54a
[3]: More about X-learner and how it overcomes T-learner (high variance) and S-learners (high bias): https://www.youtube.com/watch?v=88WHWv5QSWs&ab_channel=BradyNeal-CausalInference
[4] Good Discussion on when Partial Dependency Plots can be used to infer causality: https://web.stanford.edu/~hastie/Papers/pdp_zhao.pdf
[5] Blog based on 2: https://lmc2179.github.io/posts/pdp.html
[6]: CMU blog post on causality: https://blog.ml.cmu.edu/2020/08/31/7-causality/
[7] Microsoft’s blog on causal inference: https://medium.com/data-science-at-microsoft/causal-inference-part-1-of-3-understanding-the-fundamentals-816f4723e54a
[8] Advanced Discussion: https://www.inference.vc/untitled/
[9] 3 layers of the causal hierarchy: http://web.cs.ucla.edu/~kaoru/3-layer-causal-hierarchy.pdf