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Beyond the Average Treatment Effect

 * Home
 * Instructors


 1. Welcome!

 * Source Code

 * Welcome!
 * 1. Introduction
 * 2. Defining Interventions
 * 3. Estimators
 * 4. Estimating Effects with lmtp
   * The lmtp package
   * Static effects and the ATE
   * Dynamic Treatment Regimes
   * Modified Treatment Policies
   * IPSI
   * Survival Analysis
   * Multivariate Exposures
 * 5. Final Remarks




ON THIS PAGE

 * Learning objectives
 * Tentative Schedule
 * webR


ESTIMATING THE CAUSAL EFFECTS OF BINARY, CATEGORICAL, CONTINUOUS, AND
MULTIVARIATE EXPOSURES IN R

Modified

June 14, 2024

WebR Status

🟢 Ready!

> SER 2024 - Austin, Texas

In this workshop, we present methods to define and estimate the causal effects
of categorical, continuous, and multivariate exposures. The methods are based on
a generalization of the static and dynamic interventions that may be familiar to
some of you. This generalization has been recently called modified treatment
policies (MTPs). MTPs are hypothetical interventions where the post-intervention
exposure is defined as a modification of the natural value of the exposure that
can also depend on the unit’s history. This short course will introduce the lmtp
R package for estimating the causal effects of these general estimand in both
point-treatment and longitudinal studies. We will discuss identification of
MTPs, estimation with a targeted minimum-loss based estimator and a sequentially
doubly-robust estimator, and provide guidance on estimator choice and software
usage.


LEARNING OBJECTIVES

By the end of the workshop, participants will be able to:

 1. Generalize static and dynamic interventions intuitively and using notation.

 2. Estimate the effect of a static or dynamic intervention with lmtp for
    point-treatment and longitudinal studies.

 3. Estimate the effect of an MTP on a continuous-valued exposure with lmtp for
    point-treatment and longitudinal studies.

 4. Estimate the effect of multivariate exposures with lmtp for point-treatment
    and longitudinal studies.

This workshop assumes the participant has a basic understanding of fundamental
concepts in causal inference such as the concept of counterfactuals, and some
experience with the R programming language.


TENTATIVE SCHEDULE

Time Topic 1:00 PM - 1:15 PM Introductions 1:15 PM - 1:30 PM From observed data
to causal estimands 1:30 PM - 2:30 PM Defining causal effects using MTPs 2:30 PM
- 2:50 PM The estimator landscape 2:50 PM - 3:00 PM Break 3:00 PM - 3:15 PM
Setting up the correct data structure 3:15 PM - 4:45 PM Estimating effects using
the lmtp package 4:45 PM - 5:00 PM Q + A


WEBR

This workshop was prepared using Quarto and webR. The source code is available
on GitHub. webR is a version of the R programming language compiled to be run
directly in the browser. Using webR for this workshop avoids having to spend
time setting up a computing environment and making sure workshop participants
are using the same version of R and R packages.

Run Code

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