Summary
With the ongoing “data explosion”, methods to delineate causation from correlation are perhaps more pressing now than ever. This course will introduce the Causal Roadmap, which is a general framework for Causal Inference: (1) clear statement of the research question, (2) definition of the causal model and effect of interest, (3) specification of the observed data, (4) assessment of identifiability - that is, linking the causal effect to a parameter estimable from the observed data distribution, (5) specification of the statistical estimation problem, (6) choice and implementation of estimators, including state-of-the-art methods, and (7) appropriate interpretation of findings (Petersen & van der Laan, Epi, 2014; Figure). The statistical methods include G-computation, inverse probability weighting (IPW), and targeted minimum loss-based estimation (TMLE) with Super Learner, an ensemble machine learning method. The emphasis will be on practical implementation and real-world challenges and solutions. You will gain experience working through the Roadmap with case studies, R labs, R assignments, and a final project using real data. By the end of the course, you will have the practical tools to assess cause-and-effect in your applied work.
Roadmap Overview & Roadmap Step 0 - Research Question
Learning objectives:
- Identify the distinction between causal and statistical inference
- Describe the Causal Roadmap, which serves as the framework for the course
Corresponding Materials:
Roadmap Step 1 - Causal Model
Learning objectives:
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Explain how causal models encode our knowledge about the system that we are studying – including the roles of exclusion restrictions and independence assumptions
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In R, simulate data from a specific data generating process, reflected in the causal model
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Roadmap Step 2 - Counterfactuals & Causal Effects
Learning objectives:
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Explain how to formally specify our research question in terms of causal effects, which are summaries of counterfactual outcomes
- In R, generate counterfactuals and evaluate the target causal effect with simulations
- Apply Steps 0-2 of the Causal Roadmap to a case study in Journal Club 1
Corresponding Materials:
Roadmap Step 3 - Observed Data
Learning Objective:
- Explain how the observed data and statistical model are related to the causal model
- Describe the distinction between parametric, semi-parametric, and non-parametric statistical models.
Corresponding Materials:
Roadmap Step 4 - Identifiability & Step 5 - Estimation Problem
Learning Objective:
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Explain the assumptions needed to express our causal parameter as a function of the observed data distribution (i.e., a statistical parameter)
- Define the statistical estimation problem
- Apply Steps 3-5 of the Causal Roadmap to a case study in Journal Club 2
Corresponding Materials:
Roadmap Step 6A - Estimation with Gcomp
Learning Objectives:
- Explain how to implement a simple substitution estimator based on the G-computation formula
- Describe the limitations of using parametric regressions
- Apply simulations to evaluate estimator performance
Corresponding Materials:
Roadmap Step 6B - Estimation with IPW
Roadmap Step 6C - Super Learner
Learning Objectives:
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Explain the dangers of using parametric regressions and ad hoc approaches to statistical estimation and inference
- Understand and implement Super Learner, an ensemble machine learning method
Corresponding Materials:
Roadmap Step 6D - TMLE
Learning Objectives:
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Understand why machine learning is not enough for causal inference
- Describe how to implement TMLE and describe its properties
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Roadmap Step 6E - Inference & Step 7 - Interpretation
Learning Objectives:
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Implement the non-parametric bootstrap for variance estimation & confidence interval construction.
- Appropriately interpret the results of our study
Corresponding Materials:
Applying the Roadmap for Missing Data
Learning Objectives:
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Explain how the Causal Roadmap can be applied for missing data
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Describe the real-data application in the SEARCH study
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