Канал: Online Causal Inference Seminar

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

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

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

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

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

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

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

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

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

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

Raaz Dwivedi: Integrating Double Robustness into Causal Latent Factor Models

Raaz Dwivedi: Integrating Double Robustness into Causal Latent Factor Models

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

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

Andrew Yiu: Semiparametric posterior corrections

Andrew Yiu: Semiparametric posterior corrections

Chan Park: Single Proxy Control

Chan Park: Single Proxy Control

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

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

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

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

Krikamol Muandet: A Measure-Theoretic Axiomatisation of Causality

Krikamol Muandet: A Measure-Theoretic Axiomatisation of Causality

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

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

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

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

Maria Glymour: Evidence triangulation in dementia research

Maria Glymour: Evidence triangulation in dementia research

Giulio Grossi: SMaC: Spatial Matrix Completion method

Giulio Grossi: SMaC: Spatial Matrix Completion method

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

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

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

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

Ting Ye: Debiased Multivariable Mendelian Randomization

Ting Ye: Debiased Multivariable Mendelian Randomization

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

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

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

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

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

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

Elizabeth Tipton: Designing Randomized Trials to Predict Treatment Effects

Elizabeth Tipton: Designing Randomized Trials to Predict Treatment Effects

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

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

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

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

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

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

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

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

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

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

Richard Guo: Confounder selection via iterative graph expansion

Richard Guo: Confounder selection via iterative graph expansion

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

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

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

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

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

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

Ruoqi Yu: How to learn more from observational factorial studies

Ruoqi Yu: How to learn more from observational factorial studies

Caleb Miles: Two fundamental problems in causal mediation analysis

Caleb Miles: Two fundamental problems in causal mediation analysis

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

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

Andrew Gelman: Better than difference-in-differences

Andrew Gelman: Better than difference-in-differences

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

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

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

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

Keegan Harris: Strategyproof Decision-Making in Panel Data Settings

Keegan Harris: Strategyproof Decision-Making in Panel Data Settings

Richard Samworth: Optimal nonparametric testing of Missing Completely At Random

Richard Samworth: Optimal nonparametric testing of Missing Completely At Random

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

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

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

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

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

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

Niels Richard Hansen: Cyclic graphical models and causal learning

Niels Richard Hansen: Cyclic graphical models and causal learning

Interview with Philip Dawid

Interview with Philip Dawid

Robin Evans: Parameterizing and Simulating from Causal Models

Robin Evans: Parameterizing and Simulating from Causal Models

Jessica Young: Causal inference with competing events

Jessica Young: Causal inference with competing events

Tobias Freidling (University of Cambridge): Sensitivity Analysis with the R^2-calculus

Tobias Freidling (University of Cambridge): Sensitivity Analysis with the R^2-calculus

Sofia Triantafyllou: A Bayesian Method for Causal Inference with Observational and Experimental Data

Sofia Triantafyllou: A Bayesian Method for Causal Inference with Observational and Experimental Data

Christina Yu: Neighborhood Interference with Low Order Interactions under Unit Randomized Design

Christina Yu: Neighborhood Interference with Low Order Interactions under Unit Randomized Design

Ingeborg Waernbaum: Selection bias and multiple inclusion criteria in observational studies

Ingeborg Waernbaum: Selection bias and multiple inclusion criteria in observational studies

Stijn Vansteelandt: Assumption-lean Causal Modeling

Stijn Vansteelandt: Assumption-lean Causal Modeling

Lauren Dang: Integration of Observational and Randomized Controlled Trial Data

Lauren Dang: Integration of Observational and Randomized Controlled Trial Data

Moritz Hardt: From prediction to power

Moritz Hardt: From prediction to power

Ilya Shpitser: Fairness By Causal Mediation Analysis: Criteria, Algorithms, and Open Problems

Ilya Shpitser: Fairness By Causal Mediation Analysis: Criteria, Algorithms, and Open Problems

Jose Zubizarreta: Bridging Matching, Regression, and Weighting as Math Programs for Causal Inference

Jose Zubizarreta: Bridging Matching, Regression, and Weighting as Math Programs for Causal Inference

Wayne Lam: Greedy Relaxations of the Sparsest Permutation Algorithm

Wayne Lam: Greedy Relaxations of the Sparsest Permutation Algorithm

Julie Josse: Causal inference for brain trauma: leveraging incomplete observational data and RCT

Julie Josse: Causal inference for brain trauma: leveraging incomplete observational data and RCT

Karthik Rajkumar: A causal test of the strength of weak ties

Karthik Rajkumar: A causal test of the strength of weak ties

Luke Miratrix: A devil’s bargain? Repairing a Diff-in-Diff parallel trends assumption with matching

Luke Miratrix: A devil’s bargain? Repairing a Diff-in-Diff parallel trends assumption with matching

Kun Zhang: Methodological advances in causal representation learning

Kun Zhang: Methodological advances in causal representation learning

Jiaqi Zhang (MIT): Active Learning for Optimal Intervention Design in Causal Models

Jiaqi Zhang (MIT): Active Learning for Optimal Intervention Design in Causal Models

Rahul Singh: Causal Inference with Corrupted Data

Rahul Singh: Causal Inference with Corrupted Data

Biwei Huang: Latent Hierarchical Causal Structure Discovery with Rank Constraints

Biwei Huang: Latent Hierarchical Causal Structure Discovery with Rank Constraints

Fan Li: A tutorial on Bayesian causal inference

Fan Li: A tutorial on Bayesian causal inference

Lihua Lei: Double-Robust Two-Way-Fixed-Effects Regression For Panel Data

Lihua Lei: Double-Robust Two-Way-Fixed-Effects Regression For Panel Data

Vasilis Syrgkanis: Automatic Debiased Machine Learning for General Nested Functionals

Vasilis Syrgkanis: Automatic Debiased Machine Learning for General Nested Functionals

Dominik Janzing: Formal framework for quantitative Root Cause Analysis

Dominik Janzing: Formal framework for quantitative Root Cause Analysis

Samuel Wang: Uncertainty Quantification for Causal Discovery

Samuel Wang: Uncertainty Quantification for Causal Discovery

AmirEmad Ghassami: Combining Experimental and Observational Data for Long-Term Causal Effects

AmirEmad Ghassami: Combining Experimental and Observational Data for Long-Term Causal Effects

Mona Azadkia: A Fast Non-parametric Approach for Causal Structure Learning in Polytrees

Mona Azadkia: A Fast Non-parametric Approach for Causal Structure Learning in Polytrees

Bin Yu: Predictability, stability, and causality with case study of genetic drivers of heart disease

Bin Yu: Predictability, stability, and causality with case study of genetic drivers of heart disease

Mireille Schnitzer: Estimation of COVID-19 vaccine effectiveness under the test-negative design

Mireille Schnitzer: Estimation of COVID-19 vaccine effectiveness under the test-negative design

Harrison Li: A general characterization of optimal tie-breaker designs

Harrison Li: A general characterization of optimal tie-breaker designs

Tim Morrison: Optimality in multivariate tie-breaker designs

Tim Morrison: Optimality in multivariate tie-breaker designs

Tyler VanderWeele: Causal Inference and Measure Construction: Towards a New Model of Measurement

Tyler VanderWeele: Causal Inference and Measure Construction: Towards a New Model of Measurement

Shu Yang: Test-based integrative analysis for combining randomized trial and real-world data

Shu Yang: Test-based integrative analysis for combining randomized trial and real-world data

Alex Luedtke: Adversarial Monte Carlo Meta-Learning of Conditional Average Treatment Effects

Alex Luedtke: Adversarial Monte Carlo Meta-Learning of Conditional Average Treatment Effects

Neil Davies: Causal estimation via instrumental variables: no simultaneous heterogeneity assumption

Neil Davies: Causal estimation via instrumental variables: no simultaneous heterogeneity assumption

Zijian Guo: Two Stage Curvature Identification: Causal Inference with Possibly Invalid IVs

Zijian Guo: Two Stage Curvature Identification: Causal Inference with Possibly Invalid IVs

Michael Oberst: Regularizing towards Causal Invariance: Linear Models with Proxies

Michael Oberst: Regularizing towards Causal Invariance: Linear Models with Proxies

Shuangning Li: Random Graph Asymptotics for Treatment Effect Estimation under Network Interference

Shuangning Li: Random Graph Asymptotics for Treatment Effect Estimation under Network Interference

Mathias Drton: Half-Trek Criterion for Identifiability of Latent Variable Models

Mathias Drton: Half-Trek Criterion for Identifiability of Latent Variable Models

Chengchun Shi: A reinforcement learning framework for dynamic causal effects in A/B testing

Chengchun Shi: A reinforcement learning framework for dynamic causal effects in A/B testing

Yuansi Chen: Domain adaptation under structural causal models

Yuansi Chen: Domain adaptation under structural causal models

Kosuke Imai:Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment

Kosuke Imai:Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment

Dominik Rothenhäusler: Calibrated inference: account for sampling and distributional uncertainty

Dominik Rothenhäusler: Calibrated inference: account for sampling and distributional uncertainty

Luke Keele (UPenn): So Many Choices: The Comparative Performance of Statistical Adjustment Methods

Luke Keele (UPenn): So Many Choices: The Comparative Performance of Statistical Adjustment Methods

Zhimei Ren: Sensitivity Analysis of Individual Treatment Effects

Zhimei Ren: Sensitivity Analysis of Individual Treatment Effects

Sander Beckers: Causal Sufficiency and Actual Causation

Sander Beckers: Causal Sufficiency and Actual Causation

Daniel McCaffrey: Nonrandom Samples and Causal Inference

Daniel McCaffrey: Nonrandom Samples and Causal Inference

Interview with Guido Imbens

Interview with Guido Imbens

Ruoxuan Xiong: Efficient Treatment Effect Estimation under Heterogeneous Partial Interference

Ruoxuan Xiong: Efficient Treatment Effect Estimation under Heterogeneous Partial Interference

Jann Spiess: Improving Inference from Simple Instruments through Compliance Estimation

Jann Spiess: Improving Inference from Simple Instruments through Compliance Estimation

Thomas Richardson: Single World Intervention Graphs

Thomas Richardson: Single World Intervention Graphs

Linbo Wang: Causal inference on distribution functions

Linbo Wang: Causal inference on distribution functions

Jin Tian: Estimating Identifiable Causal Effects through Double Machine Learning

Jin Tian: Estimating Identifiable Causal Effects through Double Machine Learning

Xinran Li: Randomization Inference beyond the Sharp Null

Xinran Li: Randomization Inference beyond the Sharp Null

Carlos Cinelli: Transparent and Robust Causal Inference in the Social and Health Sciences

Carlos Cinelli: Transparent and Robust Causal Inference in the Social and Health Sciences

Nicola Gnecco: Causal Discovery in Heavy-Tailed Models

Nicola Gnecco: Causal Discovery in Heavy-Tailed Models

Juan Correa: Generalizing the Effect of Soft Interventions

Juan Correa: Generalizing the Effect of Soft Interventions

Colin Fogarty: Prepivoting in Finite Population Causal Inference

Colin Fogarty: Prepivoting in Finite Population Causal Inference

Eleanor Sanderson: Causal effects at multiple times through Multivariable Mendelian randomization

Eleanor Sanderson: Causal effects at multiple times through Multivariable Mendelian randomization

Youjin Lee: Evidence factors from multiple, possibly invalid, instrumental variables

Youjin Lee: Evidence factors from multiple, possibly invalid, instrumental variables

Ted Westling: Nonparametric tests of the causal null with non-discrete exposures

Ted Westling: Nonparametric tests of the causal null with non-discrete exposures

Daniel Malinsky: Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning

Daniel Malinsky: Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning

Joseph Antonelli: Heterogeneous causal effects of neighborhood policing in New York City

Joseph Antonelli: Heterogeneous causal effects of neighborhood policing in New York City

Susan Athey and Stefan Wager: Estimating Heterogeneous Treatment Effects in R

Susan Athey and Stefan Wager: Estimating Heterogeneous Treatment Effects in R

Xiaojie Mao: Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions

Xiaojie Mao: Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions

Maggie Makar: Causally motivated shortcut removal using auxiliary labels

Maggie Makar: Causally motivated shortcut removal using auxiliary labels

Anish Agarwal (MIT) and Dennis Shen (Berkeley): Synthetic Interventions

Anish Agarwal (MIT) and Dennis Shen (Berkeley): Synthetic Interventions

Johannes Textor: Causal Inference using the R package DAGitty

Johannes Textor: Causal Inference using the R package DAGitty

Armeen Taeb: Perturbations and causality in Gaussian latent variable models

Armeen Taeb: Perturbations and causality in Gaussian latent variable models

Fiammetta Menchetti: Estimating the causal effect of an intervention in a time series setting

Fiammetta Menchetti: Estimating the causal effect of an intervention in a time series setting

Alexander Volfovsky: Online experimentation for studying political polarization

Alexander Volfovsky: Online experimentation for studying political polarization

Isaiah Andrews: Inference on Winners

Isaiah Andrews: Inference on Winners

Sam Pimentel: Optimal tradeoffs in matched designs

Sam Pimentel: Optimal tradeoffs in matched designs

Stefan Wager: Treatment Effects in Market Equilibrium

Stefan Wager: Treatment Effects in Market Equilibrium

Guido Imbens: Using Experiments to Correct for Selection in Observational Studies

Guido Imbens: Using Experiments to Correct for Selection in Observational Studies

Leon Bottou: Learning Representations Using Causal Invariance

Leon Bottou: Learning Representations Using Causal Invariance

Niklas Pfister: Statistical Testing under Distributional Shifts

Niklas Pfister: Statistical Testing under Distributional Shifts

Razieh Nabi: Semiparametric inference for causal effects in graphical models with hidden variables

Razieh Nabi: Semiparametric inference for causal effects in graphical models with hidden variables

Ramesh Johari: Experimental design in two-sided platforms: an analysis of bias

Ramesh Johari: Experimental design in two-sided platforms: an analysis of bias

Corwin Zigler: Bipartite inference and air pollution transport

Corwin Zigler: Bipartite inference and air pollution transport

Sara Magliacane: Domain adaptation by using causal inference

Sara Magliacane: Domain adaptation by using causal inference

Andrea Rotnitzky: Optimal adjustment sets in non-parametric graphical models

Andrea Rotnitzky: Optimal adjustment sets in non-parametric graphical models

Alberto Abadie: A Penalized Synthetic Control Estimator for Disaggregated Data

Alberto Abadie: A Penalized Synthetic Control Estimator for Disaggregated Data

Richard Berk: Firearm Sales in California Through Interrupted Time Series Causal Analysis

Richard Berk: Firearm Sales in California Through Interrupted Time Series Causal Analysis

Kun Zhang: Learning and Using Causal Representations

Kun Zhang: Learning and Using Causal Representations

Luke Keele: Hospital Quality Risk Standardization via Approximate Balancing Weights

Luke Keele: Hospital Quality Risk Standardization via Approximate Balancing Weights

Fredrik Sävje: Balancing covariates in randomized experiments using the Gram-Schmidt Walk

Fredrik Sävje: Balancing covariates in randomized experiments using the Gram-Schmidt Walk

Fan Li: Causal Mediation Analysis for Sparse and Irregular Longitudinal Data

Fan Li: Causal Mediation Analysis for Sparse and Irregular Longitudinal Data

Donald Green: Using Placebo-Controlled Designs to Detect Edutainment Effects and Spillovers

Donald Green: Using Placebo-Controlled Designs to Detect Edutainment Effects and Spillovers

Interview with James Robins (Harvard)

Interview with James Robins (Harvard)

Stephen Bates: Causal Inference in Genetic Trio studies

Stephen Bates: Causal Inference in Genetic Trio studies

Mark van der Laan: Higher order Targeted Maximum Likelihood Estimation

Mark van der Laan: Higher order Targeted Maximum Likelihood Estimation

Susan Athey: Synthetic Difference in Differences

Susan Athey: Synthetic Difference in Differences

Luke Miratrix: Getting a handle on how bad some biases might be in practice

Luke Miratrix: Getting a handle on how bad some biases might be in practice

Qingyuan Zhao: Selection Bias in 2020

Qingyuan Zhao: Selection Bias in 2020

Vanessa Didelez: Causal reasoning in survival and time-to-event analyses

Vanessa Didelez: Causal reasoning in survival and time-to-event analyses

Fan Li: Propensity score weighting for covariate adjustment in randomized clinical trials

Fan Li: Propensity score weighting for covariate adjustment in randomized clinical trials

Interview with Judea Pearl (UCLA)

Interview with Judea Pearl (UCLA)

Naïve regression requires weaker assumptions than factor models to adjust for multicause confounding

Naïve regression requires weaker assumptions than factor models to adjust for multicause confounding

Interview with Donald Rubin (Harvard)

Interview with Donald Rubin (Harvard)

David Blei: The Deconfounder: What is it? What is its theory? Is it useful?

David Blei: The Deconfounder: What is it? What is its theory? Is it useful?

Ismael Mourifie: Testing Identification assumptions in Fuzzy Regression Discontinuity Designs

Ismael Mourifie: Testing Identification assumptions in Fuzzy Regression Discontinuity Designs

Interview with Esther Duflo (MIT)

Interview with Esther Duflo (MIT)

Peng Ding: Randomization and Regression Adjustment

Peng Ding: Randomization and Regression Adjustment

Emilija Perkovic: Causal effects in maximally oriented partially directed acyclic graphs (MPDAGs)

Emilija Perkovic: Causal effects in maximally oriented partially directed acyclic graphs (MPDAGs)

Online Causal Inference Joint Seminar: Falco Bargagli Stoffi and Eli Ben-Michael

Online Causal Inference Joint Seminar: Falco Bargagli Stoffi and Eli Ben-Michael

Nathan Kallus and Xiaojie Mao: Localized Debiased Machine Learning

Nathan Kallus and Xiaojie Mao: Localized Debiased Machine Learning

Joris Mooij: Joint Causal Inference: A Unifying Perspective on Causal Discovery

Joris Mooij: Joint Causal Inference: A Unifying Perspective on Causal Discovery

David Hirshberg: Balance in Causal Inference: Poststratification to Regularized Riesz Representers

David Hirshberg: Balance in Causal Inference: Poststratification to Regularized Riesz Representers

Cynthia Rudin, Alexander Volfovsky, Sudeepa Roy: Almost Matching Exactly

Cynthia Rudin, Alexander Volfovsky, Sudeepa Roy: Almost Matching Exactly

Judith Lok: Causal organic indirect and direct effects

Judith Lok: Causal organic indirect and direct effects

Panos Toulis: Randomization tests for spillovers under general interference

Panos Toulis: Randomization tests for spillovers under general interference

Andrew Gelman: 100 Stories of Causal Inference

Andrew Gelman: 100 Stories of Causal Inference

Online Causal Inference Joint Seminar: Georgia Papadogeorgou and Lihua Lei

Online Causal Inference Joint Seminar: Georgia Papadogeorgou and Lihua Lei

Yiqing Xu and Xun Pang: A Bayesian Alternative to Synthetic Control for Comparative Case Studies

Yiqing Xu and Xun Pang: A Bayesian Alternative to Synthetic Control for Comparative Case Studies

Ya Xu: Causal Inference Challenges in Industry: A perspective from experiences at LinkedIn

Ya Xu: Causal Inference Challenges in Industry: A perspective from experiences at LinkedIn

Michal Kolesár: Finite-Sample Optimal Estimation and Inference on Average Treatment Effects

Michal Kolesár: Finite-Sample Optimal Estimation and Inference on Average Treatment Effects

Caroline Uhler: Causal inference in the light of drug repurposing for COVID-19

Caroline Uhler: Causal inference in the light of drug repurposing for COVID-19

Marloes Maathuis: Combining causal structure learning and covariate adjustment

Marloes Maathuis: Combining causal structure learning and covariate adjustment

Eytan Bakshy: Efficient Experimentation and Inference for Large Decision Spaces

Eytan Bakshy: Efficient Experimentation and Inference for Large Decision Spaces

Jonas Peters: Causality and Distribution Generalization

Jonas Peters: Causality and Distribution Generalization

Dean Eckles: Noise induced randomization in regression discontinuity designs

Dean Eckles: Noise induced randomization in regression discontinuity designs

Paul Rosenbaum: Replication and Evidence Factors in Observational Studies

Paul Rosenbaum: Replication and Evidence Factors in Observational Studies

Susan Murphy: Inference for Batched Bandits

Susan Murphy: Inference for Batched Bandits

Ilya Shpitser: Identification and estimation in graphical models of missing data

Ilya Shpitser: Identification and estimation in graphical models of missing data

Eric Tchetgen Tchetgen: Selective Machine Learning of Doubly Robust Functionals

Eric Tchetgen Tchetgen: Selective Machine Learning of Doubly Robust Functionals

Edward Kennedy: Optimal doubly robust estimation of heterogeneous causal effects

Edward Kennedy: Optimal doubly robust estimation of heterogeneous causal effects

Elizabeth Ogburn: Social network dependence, unmeasured confounding, and the replication crisis

Elizabeth Ogburn: Social network dependence, unmeasured confounding, and the replication crisis

Dylan Small: Testing an Elaborate Theory of a Causal Hypothesis

Dylan Small: Testing an Elaborate Theory of a Causal Hypothesis

Hyunseung Kang: Inferring Treatment Effects After Testing Instrument Strength in Linear Models

Hyunseung Kang: Inferring Treatment Effects After Testing Instrument Strength in Linear Models

Elizabeth Tipton: Will this Intervention Work in this Population?

Elizabeth Tipton: Will this Intervention Work in this Population?