Demystifying Invited Session Proposals

Invited sessions at conferences provide important opportunities for the exchange of ideas. But how do we get invited? And how can we do the inviting? In this panel, we bring together experienced women in statistics from all career stages to share their tips on organizing invited sessions. Our panelists have planned and participated in numerous successful invited sessions at statistical conferences and have served on program committees to plan and select these sessions on a large scale. This session, sponsored by the Caucus for Women in Statistics, is intended to demystify the invited session proposal process and to empower researchers to submit their ideas in the future.

Causal Quartet: When Statistics Alone Do Not Tell the Full Story

This talk will delve into two major causal inference obstacles: (1) identifying which variables to account for and (2) assessing the impact of unmeasured variables. The first half of the talk will showcase a Causal Quartet. In the spirit of Anscombe’s Quartet, this is a set of four datasets with identical statistical properties, yet different true causal effects due to differing data generating mechanisms. These simple datasets provide a straightforward example for statisticians to point to when explaining these concepts to collaborators and students. The second half of the talk will focus on how statistical techniques can be leveraged to examine the impact of a potential unmeasured confounder. We will examine sensitivity analyses under several scenarios with varying levels of information about potential unmeasured confounders, introducing the tipr R package, which provides tools for conducting sensitivity analyses in a flexible and accessible manner.

Estimating Causal Effects: This be Madness, Yet There is Method in It

This talk will delve into two major causal inference obstacles: (1) identifying which variables to account for and (2) assessing the impact of unmeasured variables. The first half of the talk will showcase a Causal Quartet. In the spirit of Anscombe’s Quartet, this is a set of four datasets with identical statistical properties, yet different true causal effects due to differing data generating mechanisms. These simple datasets provide a straightforward example for statisticians to point to when explaining these concepts to collaborators and students. To adjust or not adjust, that is the question; we demonstrate that statistics alone cannot be used to establish which variables to adjust for when estimating causal effects. The second half of the talk will focus on how statistical techniques can be leveraged to address unmeasured confounding. We will examine sensitivity analyses under several scenarios with varying levels of information about potential unmeasured confounders. These techniques will be applied using the tipr R package, which provides tools for conducting sensitivity analyses in a flexible and accessible manner.

A Visual Diagnostic Tool for Causal Inference

This talk covers several diagnostic plots that are helpful when attempting to answer a causal question. They can be used to visualize the target population, balance, and treatment effect heterogeneity.

Causal Inference in R

In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting.

Causal Inference is Not a Statistics Problem

In this session, Dr. Lucy D’Agostino McGowan 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. As part of this talk, Lucy 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.

Causal Inference is Not Just a Statistics Problem

This lecture will explore two major challenges in causal inference: (1) how to determine which variables to adjust for and (2) how to assess the impact of unmeasured variables. The first half of the talk 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. Then we will discuss sensitivity analyses for unmeasured confounders, showcasing the tipr R package.

Causal Inference Challenges that Go Beyond Statistics

This talk will delve into two major causal inference obstacles: (1) identifying which variables to account for and (2) assessing the impact of unmeasured variables. The first half of the talk will showcase a Causal Quartet. In the spirit of Anscombe’s Quartet, this is a set of four datasets with identical statistical properties, yet different true causal effects due to differing data generating mechanisms. These simple datasets provide a straightforward example for biostatisticians to point to when explaining these concepts to collaborators and students. Here, statistics can’t solve your causal inference problem because statistics alone cannot be used to establish which variables to adjust for when estimating causal effects. Statistics can help us explore the impact of unmeasured variables. The second half of the talk will focus on how statistical techniques can be leveraged to address unmeasured confounding. We will examine sensitivity analyses under several scenarios with varying levels of information about potential unmeasured confounders. These techniques will be applied using the tipr R package, which provides tools for conducting sensitivity analyses in a flexible and accessible manner.

The Science of ChatGPT

A panel of faculty experts on machine learning, language learning, neurobiology, and philosophy of mind help us understand how ChatGPT and large language models work.

Kickstart your Career: How to Maximize Those Early Years

The transition from graduate school to navigate the unknowns of the job market is challenging for every new statistician. Proper training, efficient networking, and building a professional profile are some of the early initiatives to prepare graduate students for this change. Statisticians are trained in modeling and data analysis; however, the real-world job market requires skills beyond technical knowledge, including communication, presentation, leadership, and collaborative skills, as well as pitching one’s ideas and goals, and being able to advocate for oneself. As a group of emerging statisticians, CENS would like to fill this gap and invite early-career statisticians to discuss the unique challenges that early-career statisticians might face in a new work environment and how to deal with them. Our panel includes both academic and industry statisticians at the MS and PhD level, several of whom graduated in the last five years. Their valuable insights and mentoring guidance will be useful for newly-emerging statisticians to build a path to kickstart their careers achieving their goals.