00:04 Efficient structure learning with automatic sparsity selection for causal graph processes - Théophile Griveau-Billion 02:00 De-biased Machine Learning for Compliers - Liyang Sun 05:11 Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation - Zichen (Vincent) Zhang 07:00 Estimating the long-term effect of early treatment initiation in Parkinson’s disease using observational data - Jesse Krijthe 08:43 Causal datasheet: An approximate guide to practically assess Bayesian networks in the real world - Vincent S Huang 11:12 A Sequence of Two Studies to Propose & Test Sub-groups with Heterogeneous Treatment Effects - Rahul Ladhania 13:31 EconML: A Machine Learning Library for Estimating Heterogeneous Treatment Effects - Miruna Oprescu 15:18 Counterfactual diagnosis - Jonathan Richens 17:15 Machine Learning for Dynamic Discrete Choice - Vira Semenova