Modeling Heterogeneous Treatment Effects with R Published 2018-07-14 Download video MP4 360p Recommendations 30:39 Inferring the effect of an event using CausalImpact by Kay Brodersen 17:06 An intuitive introduction to Propensity Score Matching 19:06 Causal Effects via Regression | w/ Python Code 1:04:43 Susan Athey and Stefan Wager: Estimating Heterogeneous Treatment Effects in R 14:18 Average Treatment Effect (ATE) vs. Average Treatment effect on the Treated (ATT) 27:28 What is causal inference, and why should data scientists know? by Ludvig Hult 54:11 GitHub Copilot in Rstudio, it's finally here! 16:09 Causal Inference with Machine Learning - EXPLAINED! 2:12:16 useR! 2020: Causal inference in R (Lucy D'Agostino McGowan, Malcom Barrett), tutorial 1:06:09 Keynote: Judea Pearl - The New Science of Cause and Effect 57:10 Conditional Average Treatment Effects: Overview 22:46 Average Treatment Effects: Introduction 04:46 Treatment effects in Stata®: Propensity-score matching 06:56 Average Treatment Effects: Causal Inference Bootcamp 05:52 Causality: Fixed Effects 03:54 Causal Trees 10:17 Treatment Effects (The Effect, Videos on Causality, Ep. 21) Similar videos 09:11 Estimating Heterogeneous Treatment Effects (The Effect, Videos on Causality, Ep 66) 10:08 Analysis of heterogeneity of treatment effects with causal forests using the grf package in R 57:02 Susan Athey Guest Talk - Estimating Heterogeneous Treatment Effects 47:39 Causal Inference -- 10/23 -- Heterogeneous Treatment Effects and Target Parameters (ATE, ATT, etc) 18:08 Modeling Heterogeneous Preferences 3:28:55 AI and Causality: Estimating Heterogeneous Treatment Effects, Interpretability and Explainability 59:40 Akash Issar presents “Two-way fixed effects estimators with heterogeneous treatment effects” 32:37 ITE inference - meta-learners for CATE estimation 09:02 Staggered Treatment in Difference-in-Differences (The Effects, Videos on Causality, Ep 56) 20:25 Modeling Heterogeneous Preferences (old) 15:55 Simulation Studies in R 03:06 3.2 (a) Average Treatment Effect (ATE) 04:39 Identification, Part 3: Instrumental Variables 05:59 Unobserved heterogeneity 29:29 Causal Inference -- 9/23 -- Heckman Selection Model More results