## Summary

## Course Learning Objectives

By the end of this course, students should be able to

1. Translate your research question and knowledge into a causal model (directed acyclic graphs and non-parametric structural equation models).

2. Define the target causal parameter with counterfactuals.

3. Assess identifiability of the target causal parameter and express it as a parameter of the observed data distribution.

4. Explain the challenges posed by parametric estimation approaches and apply machine learning methods.

5. Identify the properties of and apply three estimators: G-computation, inverse probability weighting (IPW), and targeted minimum loss-based estimation (TMLE) with Super Learner.

6. Explain how to appropriately address missing outcomes, which may be differentially measured.

7. Apply course concepts to address cause-and-effect in a real-data application in your final projects.

8. Explore more advanced settings for Causal Inference, such as time-dependent exposures, clustered data, and continuous exposures.

**Roadmap Overview & Step 0 Research Question **

**Learning objectives: 1. **Identify the distinction between causal and statistical inference

**2.** Describe the Causal Roadmap, which serves as the framework for the course

**Corresponding Materials:Lecture 1 **

**Step 1 Causal Model**

**Learning objectives: ****1.** Explain how causal models encode our knowledge about the system that we are studying – including the roles of exclusion restrictions and independence assumptions

**2**. In R, simulate data from a specific data generating process, reflected in the causal model. **Corresponding Materials:****Lecture 2**

**R HW1A**

**Step 2 Causal Parameter**

**Learning objectives: ****1**. Explain how to formally specify our research question in terms of causal effects, which are summaries of counterfactual outcomes

**2.** In R, generate counterfactuals and evaluate the target causal effect with simulations

**3**. Apply Steps 0-2 of the Causal Roadmap to a case study in Journal Club 1.**Corresponding MaterialsLecture 3 & Lecture 4R HW 1BJournal Club Assignment 1**

**Step 3 Observed Data & Statistical Model**

**Steps 4 & 5 Identifiability & the Statistical Estimation Problem**

**Learning Objective:1.** Explain the assumptions needed to express our causal parameter as a function of the observed data distribution (i.e., a statistical parameter)

**2**. Define the statistical estimation problem

**3.**Apply Steps 3-5 of the Causal Roadmap to a case study in Journal Club 2.

**Corresponding Materials:**

Lecture 6

R HW 2b

Journal Club Assignment 2R Lab 2B

Lecture 6

R HW 2b

Journal Club Assignment 2R Lab 2B

**Step 6a Estimation with G-computation **

**Learning Objectives:****1.**Explain how to implement a simple substitution estimator based on the G-computation formula**2.** Describe the limitations of using parametric regressions**3.** Apply simulations to evaluate estimator performance**Corresponding Lectures:Lecture 8R Lab 2CR HW 2C**

**Step 6b Estimation with Inverse Probability Weighting**

**Step 6c Estimation with Super Learner**

**Learning Objectives:1.** Explain the dangers of using parametric regressions and ad hoc approaches to statistical estimation and inference

**2.**Understand and implement Super Learner, an ensemble machine learning method

**Corresponding Lectures:**

**Lecture 10**

R Lab 4

R HW 4 & Supporting Document

R Lab 4

R HW 4 & Supporting Document

**Step 6d Estimation with TMLE **

**Learning Objectives: ****1.** Understand why machine learning is not enough for causal inference**2.** Describe how to implement TMLE and describe its properties**Corresponding Lectures:Lecture 12R Lab 5 & Supporting Document **

**Steps 6e & 7 Inference & Interpretation**

**Learning Objectives:1.** Implement the non-parametric bootstrap for variance estimation & confidence interval construction.

**2.**Appropriately interpret the results of our study

**Corresponding Lectures:****Lecture 13R Lab 6 & Supporting Document **

**Applying the Roadmap for Missing Data**

**Learning Objectives:1. **Explain how the Causal Roadmap can be applied for missing data

**2.**Describe the real-data application in the SEARCH study.

**Corresponding Lectures:Lecture 14**

**More Advanced Topics**

**Learning Objectives: 1. **Describe how the Causal Roadmap can be applied in more advanced settings

**Corresponding Lectures:**

Lecture 15

Closing thoughts about the Evolution of Causal Inference

**Final Project**

**Final Project Guidelines: Fully apply each step of the causal roadmap to a real-world problem. **

## Readings

Suggested background readings for each topic/section of the course are provided. Helpful references are also provided at appropriate points in the lecture slides. Please note that the listed references are NOT intended as a complete bibliography, but only as helpful entry points to the material.

1. M.J. van der Laan and S. Rose. Targeted Learning: Causal Inference for Observational and Experimental Data. Springer, Berlin Heidelberg New York,2011.

○ Available for UC Berkeley students here

2. J. Pearl. Causality. Models, Reasoning, and Inference. Cambridge University

Press, 2000, 2nd Ed 2009.

○ Available for UC Berkeley students here

**We would like to thank Mark van der Laan for his contributions to the development of this course. We would also like to thank the former Graduate Student Instructors (GSIs) and our students for their valuable feedback to the course content and organization.**

**Suggested citation for the course: **

**M. Petersen and L. Balzer. Introduction to Causal Inference. UC Berkeley, August 2014. <www.ucbbiostat.com>**

**Introduction to Causal Inference by Maya Petersen & Laura Balzer is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. **

This work by CTML Faculty and/or Staff is licensed under CC BY 4.0