The intuition behind the propensity score and inverse probability weighting.

First video in my series on the propensity score.
All videos on the propensity score: [ Ссылка ]-

I introduce two techniques to solve the Fundamental Problem of Causal Inference: Regression adjustment & Inverse Probability Weighting (or Propensity Weighting). Both rely on observable variables to remove the Selection Bias. I speak about the intuition behind the Inverse Probability Weighting to see how exactly it overcomes the Fundamental Problem.

Video was created using the software and associated resources of Movavi: [ Ссылка ].

Bibliography:
Alves, M. F. Causal Inference for the Brave and True. Online, 2022. Retrieved from [ Ссылка ] [09 February 2023]
Angrist, J. D., and Jorn-Steffen Pischke. Mostly Harmless Econometrics. Princeton, NJ: Princeton University Press, 2008.
Cunningham, Scott. Causal Inference The Mixtape. New Haven, CT: Yale University Press, 2021. [ Ссылка ].
Hernán, M. A., and Robins, J. M. Causal Inference What If. Boca Raton: Chapman & Hall/CRC, 2020.
Huntington-Klein, N. (2022). The Effect An Introduction to Research Design and Causality. London, England: Taylor & Francis.

00:00 Introduction
02:03 Fundamental Problem Recapped
03:16 Selection on Observables
10:31 Regression Adjustment to Solve the Fundamental Problem
13:48 Inverse Probability Weighting to Solve the Fundamental Problem
19:26 Interpreting Inverse Probability Weighting
23:33 Summary

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