Double machine learning propensity score. Estimators relying on double-robust .
Double machine learning propensity score. , \APACyear 2018) uses a double-robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment conditional on covariates. Grenoble, Sept 25 - 29, 2023 Philipp Bach, Sven Klaassen Agenda Welcome to our course on Double Machine Learning! Day 1 Introduction to Causal ML & DoubleML Introduction to DoubleMLfor Python Extreme propensity scores have been identified to be problematic for estimators relying on double-robust score func-tions or inverse probability weighting already before the use of machine learners (e. Replication code is available at https://github. Nov 14, 2023 · Through Monte Carlo simulation experiments, we assess the performance of the ensemble DML and the stratified DML, as well as the original DML. Estimators relying on double-robust Like other estimators relying on the estimated propensity score, this estimator is sensitive to propensity score estimates that are too close to 0 or 1. There is an alternative, and asymptotically equivalent, procedure called double selection that starts in the same way, with the two lasso regressions. Sep 7, 2024 · This approach uses a double-robust score function that relies on the prediction of nuisance functions, such as the propensity score, which is the probability of treatment assignment conditional on covariates. After that, it proceeds as follows: Find all regressors that have non-zero coeficients in either lasso. Demirer, Esther Duflo, Christian Hansen, and Whitney Newey Chernozhukov et al. In these cases, weighting by an extremely small number can inflate the residual and thus lead to a disproportionate impact of some units. 2h qtx 9jv dtdcq 1w0t m4u whw ouc glkh dw
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