Causal Inference

Unit 1 – Probability and Statistics Fundamentals

Unit 2 – Potential outcomes framework

Unit 3 – Randomized experiments

Unit 4 – Observational Studies & Confounding

Unit 5 – Matching and propensity scores

Unit 6 – Instrumental variables

Unit 7 – Regression discontinuity designs

Unit 8 – Difference-in-Differences & Panel Data Methods

Unit 9 – Causal Graphs & Structural Models

Unit 10 – Causal Discovery & Learning Algorithms

Unit 11 – Social Science & Policy Evaluation Applications

Unit 12 – Causal Inference: Limitations and Frontiers

## What do you learn in Causal Inference

Causal Inference is all about figuring out cause-and-effect relationships in data. You'll learn how to tell if A actually causes B, or if they're just hanging out together by chance. The course covers experimental design, randomized trials, observational studies, and fancy statistical methods like propensity score matching and instrumental variables.

## Is Causal Inference hard?

People often think Causal Inference is super tough because it involves a lot of stats and some heavy-duty math. The reality? It can be challenging, but it's not impossible. The concepts can get pretty abstract, and there's a fair bit of critical thinking involved. But if you've got a decent stats background and you're willing to put in the work, you'll be fine.

## Tips for taking Causal Inference in college

1. Use [Fiveable Study Guides](https://fiveable.me/cram-mode) to help you cram. 🌶️
2. Practice, practice, practice with real-world examples. Try to spot potential confounders in news articles about scientific studies.
3. Get comfortable with DAGs (Directed Acyclic Graphs). They're like the secret weapon of causal inference.
4. Don't just memorize formulas - understand the logic behind them. Knowing why you're using a certain method is key.
5. Form a study group. Talking through complex concepts with classmates can really help them stick.
6. Check out "The Book of Why" by Judea Pearl for a more accessible intro to causal thinking.

## Common pre-requisites for Causal Inference

1. Probability Theory: This course dives into the mathematical foundations of probability. You'll learn about random variables, distributions, and expectation - all crucial for understanding causal inference.

2. Linear Regression: Here, you'll get familiar with modeling relationships between variables. It's a stepping stone to more advanced causal inference techniques.

3. Experimental Design: This class covers how to set up experiments to test hypotheses. It's super relevant for understanding randomized controlled trials in causal inference.

## Classes similar to Causal Inference

1. Machine Learning: This course focuses on algorithms that can learn from and make predictions on data. It shares some overlap with causal inference, especially in areas like prediction and feature selection.

2. Econometrics: While more focused on economic applications, econometrics also deals with causal relationships in observational data. You'll see some similar techniques to those in causal inference.

3. Bayesian Statistics: This class introduces a different approach to statistical inference. It's relevant to causal inference, especially when dealing with uncertainty in causal estimates.

4. Time Series Analysis: This course looks at data collected over time. While not directly causal, it deals with related concepts like forecasting and temporal dependencies.

## Majors related to Causal Inference

1. Statistics: Focuses on collecting, analyzing, and interpreting data. Causal inference is a key part of advanced statistical thinking.

2. Data Science: Combines stats, computer science, and domain knowledge to extract insights from data. Causal reasoning is increasingly important in this field.

3. Economics: Uses statistical methods to understand economic phenomena. Causal inference is crucial for policy evaluation and understanding economic relationships.

4. Epidemiology: Studies the distribution and determinants of health-related events. Causal inference is central to understanding disease causes and interventions.

## What can you do with a degree in Causal Inference?

1. Data Scientist: Analyze complex datasets to extract insights and inform decision-making. You'll use causal inference skills to separate correlation from causation in big data.

2. Policy Analyst: Evaluate the effectiveness of policies and programs. Causal inference techniques are key to understanding policy impacts.

3. Biostatistician: Design and analyze clinical trials and health studies. Your causal inference skills will be crucial in determining treatment effects.

4. Economic Consultant: Provide expert analysis on economic issues. You'll use causal inference to understand market dynamics and predict outcomes of economic policies.

## Causal Inference FAQs

1. Can I take this course if I'm not a stats major? While a strong stats background helps, students from other quantitative fields often take and succeed in this course.

2. How is this different from regular statistics? Causal inference goes beyond just finding correlations - it's about establishing cause-effect relationships, which requires special techniques.

3. Will we use any specific software in this course? Most causal inference courses use statistical software like R or Python, but it varies by instructor.

4. Is this course relevant for machine learning? Absolutely! Causal inference is becoming increasingly important in AI and ML for making more robust and interpretable models.

Statistics

1.1 Probability theory

1.3 Sampling and estimation

1.2 Random variables and distributions

1.4 Hypothesis testing

1.5 Regression analysis

2.2 Average treatment effect (ATE)

2.1 Counterfactuals and potential outcomes

2.4 Assumptions: Stable Unit Treatment Value Assumption (SUTVA) and consistency

2.3 Conditional average treatment effect (CATE)

3.1 Completely randomized designs

3.2 Stratified and blocked designs

3.3 Factorial designs

3.4 Cluster randomized designs

3.5 Analysis of randomized experiments

4.1 Confounding variables

4.4 Sensitivity analysis

4.2 Selection bias

4.3 Controlling for confounding: Stratification and regression adjustment

5.1 Propensity score

5.2 Matching methods

5.3 Inverse probability weighting

5.4 Doubly robust estimation

6.3 Local average treatment effect (LATE)

6.4 Weak instruments and identification

6.1 Instrumental variables (IV) assumptions

6.2 Two-stage least squares (2SLS)

7.1 Sharp and fuzzy regression discontinuity

7.2 Bandwidth selection and local polynomial regression

7.3 Validity tests and sensitivity analysis

8.1 Difference-in-differences (DiD) estimation

8.2 Parallel trends assumption

8.3 Fixed effects models

8.4 Synthetic control methods

9.3 Structural causal models (SCMs)

9.4 Interventions and do-calculus

9.1 Directed acyclic graphs (DAGs)

9.2 d-separation and backdoor criterion

10.1 Constraint-based algorithms

10.2 Score-based algorithms

10.3 Hybrid algorithms

10.4 Causal feature selection

11.2 Education and social programs

11.3 Epidemiology and public health

11.4 Marketing and advertising

11.1 Economics and labor markets

12.4 Machine learning for causal inference

12.3 Causal inference with complex data structures

12.1 Unmeasured confounding

12.2 External validity and generalizability

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