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