#### SCI Shortcourses will take place at the 2023 American Causal Inference Conference (ACIC), held in Austin, Texas, on Friday, May 26 from 12:00PM to 4:30PM.

#### There will also be 4 virtual Shortcourses held following the ACIC.

### RATES

Student Member – **$80**

Non-Student Member – **$110**

Student Non-Member – **$180**

Non-Student Non-Member – **$210**

# In-Person Workshops

### CAUSAL INFERENCE FOR MULTIPLE TIME-POINT (LONGITUDINAL) EXPOSURES

**INSTRUCTORS:**

Laura Balzer, University of California at Berkeley

Lina Montoya, University of North Carolina at Chapel Hill

**LOCATION:** Waller Ballroom AB

**DESCRIPTION:**

This workshop applies the Causal Roadmap to estimate the causal effects with multiple intervention variables, such as the cumulative effect of an exposure over time and the effects on survival-type outcomes with right-censoring. Read more

### MACHINE LEARNING & NONPARAMETRIC EFFICIENCY IN CAUSAL INFERENCE

**INSTRUCTORS:**

Edward Kennedy, Carnegie Mellon University

**LOCATION:** Waller Ballroom C

**DESCRIPTION:**

This short course covers the basics of efficient nonparametric estimation in causal inference, including estimating equations, TMLE, and double machine learning. It considers nonparametric efficiency bounds for causal estimands, and efficient bias-corrected estimators based on influence functions. Read more

### CAUSAL GRAPHICAL METHODS FOR HANDLING NONIGNORABLE MISSING DATA

**INSTRUCTORS:**

Razieh Nabi, Emory University

**LOCATION:** Waller Ballroom EF

**DESCRIPTION:**

It is often said that the fundamental problem of causal inference is a missing data problem. The focus of this course is on the implications of the converse view: that missing data problems are a form of causal inference. Read more

# Virtual Workshops

### DESIGNING AND IMPLEMENTING SIMULATIONS IN R

**DATE:** June 6, 2023

**INSTRUCTORS:**

Luke Miratrix, Harvard University Graduate School of Education

**DESCRIPTION:**In this course we will learn how to write Monte Carlo simulations in R. Monte Carlo simulations are an essential tool of inquiry for quantitative methodologists and students of statistics, useful both for small-scale or informal investigations and for formal methodological research. Read more

### AN INTRODUCTION TO DATA-DRIVEN SELECTION OF CAUSAL GRAPHICAL MODELS (A.K.A. CAUSAL DISCOVERY)

**DATE:** June 8, 2023

**INSTRUCTORS:**

Daniel Malinsky, Columbia University

**DESCRIPTION:**Graphical models such as directed acyclic graphs (DAGs), play an important role in causal inference. They are used to express key assumptions about the data-generating process, identify possible sources of bias, derive identification results, select adjustment variables, and in various ways support inference for causal effects. Read more

### CAUSAL INFERENCE WITH SATELLITE DATA

**DATE:** July 7, 2023

**INSTRUCTORS:**

Connor Jerzak, The University of Texas at Austin

**DESCRIPTION:**Satellite and remote sensing data has become an increasingly important data source used by governments and non-profit organizations to assist decision-making about anti-poverty assistance, natural disaster relief, agricultural planning, and in a host of other applications. Read more

### BEYOND THE ATE: ESTIMATING THE CAUSAL EFFECTS OF BINARY, CATEGORICAL, CONTINUOUS, AND MULTIVARIATE EXPOSURES IN R USING THE LMTP PACKAGE

**DATE:** July 11, 2023

**INSTRUCTORS:**

Nicholas Williams, Columbia University

**DESCRIPTION:**Modified treatment policies (MTPs) are a class of interventions that generalize static and dynamic interventions for categorical, continuous, and multivariate exposures. Read more

### CAUSAL MEDIATION ANALYSIS

**DATE:** July 19, 2023

**INSTRUCTORS:**

Boris Sobolev, The University of British Columbia

**DESCRIPTION:**Not all causal questions are answered by randomized trials. Take mediation, for example. We can’t randomize patients into groups defined by the combination of treatment and mediator values, because mediator values are the result of treatment assignment. Read more