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hey folks welcome back this is the second video in a series on causal effects in the last video we learned some theoretical Concepts that underlie causal effects however there were questions surrounding how to translate this Theory into practice in this video we will resolve these questions with a set of practical techniques for estimating causal effects these techniques are all based on something called a propensity score we will conclude the discussion with a concrete example with python code and real world data so with that letamp;#39;s get into the video so in the last video of this series we were talking about estimating causal effects and to estimate causal effects we need data but not all data are equal and so here Iamp;#39;m going to distinguish two types of ways we can obtain data so the first is data from what Iamp;#39;ll call an observational study so an observational study insists of passively measuring data without intervention in the data generating process so as an ex
