Double/Debiased Machine Learning: Combining ML with Causal Inference

Learn about Double/Debiased Machine Learning (DML), one of the most influential econometric methods of the past decade with nearly 5,000 citations. This video explains how to use ML algorithms like LASSO for causal inference without introducing regularization bias. Slides used in the video are here: https://raw.githack.com/tyleransom/st... Source code for the slides is here: https://github.com/tyleransom/structu... Link to Stata resources for implementing this approach: https://statalasso.github.io/ Scientific References: Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins, Double/debiased machine learning for treatment and structural parameters, The Econometrics Journal, Volume 21, Issue 1, 1 February 2018, Pages C1–C68, https://doi.org/10.1111/ectj.12097 Mullainathan, Sendhil, and Jann Spiess. 2017. "Machine Learning: An Applied Econometric Approach." Journal of Economic Perspectives 31 (2): 87–106. DOI: 10.1257/jep.31.2.87 Key topics: Why standard ML methods fail for causal inference The regularization bias problem How DML orthogonalizes treatment using sample splitting Cross-fitting procedure for unbiased estimation Connection to control function methods Tyler Ransom is an Associate Professor of Economics at the University of Oklahoma. Subscribe for more videos on data science, econometrics, and research methods! #doublemachinelearning #debiasedmachinelearning #DDML #machinelearningeconometrics #causalinference #LASSOregression #regularizationbias #highdimensionaleconometrics #treatmenteffects #Chernozhukov #samplesplitting #crossfitting #orthogonalization #controlfunctions #FrischWaughLovelltheorem #unconfoundedness #appliedeconometrics #modelselection #semiparametricestimation #econometricmethods #causalML #Stataeconometrics #machinelearningforeconomists #structuralparameters #omittedvariablebias #confoundingvariables #economicresearchmethods