01:01:45

Corwin Zigler: Causal health impacts of power plant emission controls ...

01:03:04

Michal Kolesár: Evaluating Counterfactual Policies Using Instruments

01:01:35

Anne Helby Petersen: Two perspectives on interpretable evaluation of causal discovery algorithms

01:01:09

Davide Viviano: Program Evaluation with Remotely Sensed Outcomes

01:13:59

Qingyuan Zhao: On statistical and causal models associated with acyclic directed mixed graphs (ADMG)

56:47

Panos Toulis & W. Guo: ML-assisted Randomization Tests for Complex Treatment Effects in A/B Expts

56:36

Yiqing Xu: Factorial Difference-in-Differences

01:00:08

Jared Murray: A Unifying Weighting Perspective on Causal Machine Learning

01:00:39

Tianchen Qian: Causal inference and machine learning in mobile health

28:37

Yuyao Wang: Learning treatment effects under covariate dependent left truncation and right censoring

30:46

Jinzhou Li: Root cause discovery via permutations and Cholesky decomposition

01:02:48

Toru Kitagawa: Policy Choice in Time-Series by Empirical Welfare Maximization

59:42

Alexis Bellot: Partial Transportability for Domain Generalization

01:02:30

Oliver Dukes: Nonparametric tests of treatment effect homogeneity for policy-makers

37:07

Bijan Mazaheri: Synthetic Potential Outcomes and the Hierarchy of Causal Identifiability

26:50

Philipp Faller: Self-compatibility: Evaluating causal discovery without ground truth

01:00:10

Anish Agarwal: Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions

01:05:03

Chao Ma: Towards Causal Foundation Model: on Duality between Causal Inference and Attention

01:05:52

Wang Miao: Introducing the specificity score: a measure of causality beyond P value

01:09:03

Rodrigo Pinto: What is causality? How to express it? And why it matters

30:48

Abhin Shah: On counterfactual inference with unobserved confounding via exponential family

27:54

Brian Gilbert: Identification/estimation of mediation effects of longitudinal modified policies

01:09:59

Raaz Dwivedi: Integrating Double Robustness into Causal Latent Factor Models

01:02:00

Hyunseung Kang: Transfer Learning Between U.S. Presidential Elections

01:06:20

Andrew Yiu: Semiparametric posterior corrections

27:12

Chan Park: Single Proxy Control

48:51

Mihaela van der Schaar: The (Causal) Discovery Ladder: Unravelling Governing Equations and Beyond

59:31

Kosuke Imai: The Cram Method for Efficient Simultaneous Learning and Evaluation

57:58

Krikamol Muandet: A Measure-Theoretic Axiomatisation of Causality

59:41

Sara Magliacane & Phillip Lippe: BISCUIT: Causal Representation Learning from Binary Interactions

01:00:13

David Lagnado: Causality in Mind: Learning, Reasoning and Blaming

01:02:24

Maria Glymour: Evidence triangulation in dementia research

30:11

Giulio Grossi: SMaC: Spatial Matrix Completion method

29:04

Xinwei Shen: Causality-oriented robustness: exploiting data heterogeneity at different levels

01:06:28

Iván Díaz: Recanting twins: addressing intermediate confounding in mediation analysis

59:52

Ting Ye: Debiased Multivariable Mendelian Randomization

01:01:21

Fan Yang: Mediation analysis with the mediator and outcome missing not at random

01:04:02

Victor Veitch: Linear Structure of (Causal) Concepts in Generative AI

59:05

Jonas Peters, Nicola Gnecco, Sorawit Saengkyongam: Invariance-based Generalization and Extrapolation

59:55

Elizabeth Tipton: Designing Randomized Trials to Predict Treatment Effects

01:06:54

Mats Stensrud & Aaron Sarvet: Interpretational errors in causal inference and how to avoid them

01:06:03

Erica Moodie: Flexible modeling of adaptive treatment strategies for censored outcomes

01:02:14

Yuqi Gu: Identifiable Deep Generative Models for Rich Data Types with Discrete Latent Layers

59:18

Maya Mathur: A common-cause principle for eliminating selection bias in causal estimands

59:20

Anish Agarwal: On Causal Inference with Temporal and Spatial Spillovers in Panel Data

01:02:42

Richard Guo: Confounder selection via iterative graph expansion

32:05

Chris Harshaw: ClipOGD: Experimental Design for Adaptive Neyman Allocation in Sequential Experiments

32:21

Michael Celentano: Challenges of the inconsistency regime: Novel debiasing methods for missing data

01:03:30

Ricardo Silva: Intervention Generalization: A View from Factor Graph Models

59:26

Ruoqi Yu: How to learn more from observational factorial studies

01:17:31

Caleb Miles: Two fundamental problems in causal mediation analysis

01:01:46

Michael Hudgens&Chanhwa Lee: Nonparametric Estimation of Policy Effects with Clustered Interference

01:15:58

Andrew Gelman: Better than difference-in-differences

30:03

Benedicte Colnet: Risk ratio, odds ratio, risk difference... Which measure is easier to generalize?

55:38

Erin Gabriel: Derivation and usefulness of tight symbolic causal bounds for ordinal outcomes

28:24

Keegan Harris: Strategyproof Decision-Making in Panel Data Settings

01:03:02

Richard Samworth: Optimal nonparametric testing of Missing Completely At Random

01:06:37

Yuhao Wang: Root-n-consistent estimators for average treatment effect with minimal sparsity

01:06:30

Thijs van Ommen: Graphical Representations for Algebraic Constraints of Linear Structural Models

01:00:47

Philipp Bach and Sven Klaassen: Tutorial on DoubleML for double machine learning in Python and R