Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal... | PyData NYC 2019
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- čas přidán 9. 07. 2024
- Full title: Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal Inference | PyData New York 2019
Propensity score matching provides an alternative framework for causal inference when random assignment is not possible. The technique draws on core data science skills of predictive model building and algorithm development. Data scientists who need alternatives to experiments will find this a useful and accessible addition to their methodological toolbox.
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00:53 Outline
01:30 Propensity Score Matching in a Nutshell
03:25 The Selection Problem
05:52 Key Steps
07:18 Goals
Good lecture by an expert in the field and the slides were well designed as well. Great talk
One question, if we get overfitting propensity scores, then the overlap we want will be very small. It looks like conflict arguments here.
Helpful lecture by data science Tucker Carlson!
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