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Instructors
Dan Sarkar, Instructor - Causal Modeling: Establishing Causal Inferences with Examples in SAS

Dan Sarkar

Instructor Dan Sarkar is a Data Science & Analytic Thought Leader, Project Manager, Data Scientist, Strategist, Speaker, Startup Adviser.
Andrew Wilson, Instructor - Causal Modeling: Establishing Causal Inferences with Examples in SAS

Andrew Wilson

Former Director of applied statistics in the University of Utah and current Director of Pharmacoepidemiology and Statistics in Real World Data at PAREXEL, a very large CRO.

In this course, we introduce an approach to making such inferences via potential outcomes.

Instructor Andrew Wilson is a Former Director of applied statistics in the University of Utah and current Director of Pharmacoepidemiology and Statistics in Real World Data at PAREXEL, a very large CRO.

Instructor Dan Sarkar is a Data Science & Analytic Thought Leader, Project Manager, Data Scientist, Strategist, Speaker, Startup Adviser.

Course Description

In this section, we introduce an approach to making such inferences via potential outcomes. We'll discuss approaches to a number of data collection designs and associated problems commonly encountered in clinical research, epidemiology, economics, etc. Topics considered include the fundamental role of the assignment mechanism, in particular, the importance of randomization as an un-confounded method of assignment, and methods for limiting bias in the analysis of observational data, including propensity score analysis.

What am I going to get from this course?

  • Understand sources of bias in nonrandomized (and even randomized) studies, and how to use directed acyclic graphs (DAGs) and propensity scores to address.
  • Similarly, understand how sampling and selection bias can arise, and how to use Heckman (two-stage) modeling to address.
  • Finally, some interventions don't have a control (like a hospital-wide intervention) and are assessed by comparison to historical  controls. One way to assess is through interrupted time series, or discontinuity regression models.
  • For all the above, participants will be able to apply (SAS) software solutions, de-bug/work out problems, and interpret in a causal context.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • Statistics with regression and introductory SAS coding experience.

Who should take this course? Who should not?

  •  Professionals with backgrounds in analytic techniques, from economists, epidemiologists, and others in quantitative sciences.

Curriculum

Module 1: Establishing Causal Inferences

Lecture 1 Confounding
Lecture 2 Cause, Causal Pathways, and DAGs
Lecture 3 Adjustment Sets and Propensity Scores
Lecture 4 Other Structural Frameworks: Mediation, Moderation and Longitudinal Models
Lecture 5 Concept Intro. Marginal Structural Models
Lecture 6 Propensity Scores
Lecture 7 Conditional Independence Assumption
Lecture 8 Estimating the Propensity Scores

Lecture 9 Casual Structural Approach ( DAG- directed) to estimate effects
Lecture 10 Longitudinal Analysis

Module 2: Selection Bias and James Heckman

Lecture 11 Two-stage Model
Lecture 12 Heckman's correction Method
Lecture 13 Handling Missing Data
Lecture 14 Censoring

Module 3: Special Applications: Interrupted Time Series

Lecture 15 Regression Discontinuity Design ( RDD) & Interrupted Time Series (ITS)
Lecture 16 Regression Models to Match
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