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Michael Johns: Propensity Score Matching: A Non-experimental Approach to Causal... | PyData NYC 2019 

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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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1 июл 2024

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Комментарии : 5   
@danielechavezs7502
@danielechavezs7502 Год назад
00:53 Outline 01:30 Propensity Score Matching in a Nutshell 03:25 The Selection Problem 05:52 Key Steps 07:18 Goals
@rembautimes8808
@rembautimes8808 2 года назад
Good lecture by an expert in the field and the slides were well designed as well. Great talk
@GuorongLi-re7kt
@GuorongLi-re7kt 12 дней назад
One question, if we get overfitting propensity scores, then the overlap we want will be very small. It looks like conflict arguments here.
@Akerfeldtfan
@Akerfeldtfan Год назад
Helpful lecture by data science Tucker Carlson!
@MMUnubi
@MMUnubi 27 дней назад
😀😀
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