In this talk we will discuss some of the major challenges in causal inference, and why statistical tools alone cannot uncover the data-generating mechanism when attempting to answer causal questions. We will showcase the Causal Quartet, which consists of four datasets that have the same statistical properties, but different true causal effects due to different ways in which the data was generated. These examples illustrate the limitations of relying solely on statistical tools in data analyses and highlight the crucial role of domain-specific knowledge.

This workshop introduces the essential elements of answering causal questions in R. Participants will work through examples of causal inference workflows, learn when standard statistical methods are appropriate and when specialized causal methods are needed, and practice specifying causal questions using Directed Acyclic Graphs (DAGs). The workshop also covers fitting, diagnosing, and applying propensity score models through weighting and matching to estimate causal effects.

Doubly robust estimators are often viewed as protected against model misspecification, but this protection can fail in the presence of missing data. When multiple imputation is used, lack of congeniality between the imputation and analysis models can induce bias even when both the propensity score and outcome models are correctly specified. Valid inference requires imputation models to include all variables from both the propensity score and outcome models, specified in compatible functional forms. Violations of these conditions can lead to substantial bias in treatment effect estimates. A general framework for combining multiple imputation with doubly robust estimation is presented, the conditions required for congeniality are characterized, and the consequences of misspecification are illustrated. The talk concludes with practical recommendations for specifying imputation models to preserve the validity of doubly robust methods in applied causal analyses.

Introduction to Regression and Data Science. Learn to explore, visualize, model, evaluate, and communicate data in a reproducible manner. Gain hands on experience with real data from a variety of disciplines. The course will focus on the statistical computing language R.

Seminar in Mathematical Business Analysis. The main purpose of this seminar is to develop the capability to apply quantitative knowledge to real and ill-defined problems. It tries to bridge the gap between the theory of quantitative decision approaches such as management science/operations research, information systems, and statistics (now mainly collected in the Business Analytics field), with the application of these approaches to the solution of actual business problems.

Causal Inference. From Correlation to Causation. The goal of this course is to give students the skills needed to conduct analyses and communicate results when causality is the goal. Students will learn how to implement causal inference techniques including matching and weighting, evaluate assumptions, and conduct sensitivity analyses.

This paper provides clear and practical guidance on the specification of imputation models when multiple imputation is used in conjunction with doubly robust estimation methods for causal inference. Through theoretical arguments and targeted simulations, we demonstrate that if a confounder has missing data, the corresponding imputation model must include all variables appearing in either the propensity score model or the outcome model, in addition to both the exposure and the outcome, and that these variables must enter the imputation model in the same functional form as in the final analysis.

Partitioned Local Depth (PaLD) is a framework for holistic consideration of community structure for distance-based data. This paper describes an R package, pald, for calculating Partitioned Local Depth (PaLD) probabilities, implementing community analyses, determining community clusters, and creating data visualizations to display community structure. We present essentials of the PaLD approach, describe how to use the pald package, walk through several examples, and discuss the method in relation to commonly used techniques.