Lectures on Causal Inference

This is a series of introductory lectures in applied econometrics with emphasis on the identification and estimation of causal effects using observational data. There is an intense discussion in economics and other social sciences about how to measure causal effects (and if it is at all possible). These lectures focuses on five methods:
  1. random assignment,
  2. multiple regression,
  3. instrumental variable regression,
  4. regression discontinuity designs, and
  5. differences in differences.

Lectures

  1. Review of probability theory
    1. Probability Theory: Single Random Variable
    2. Probability Theory: Multiple Random Variable
    3. Probability Theory: Conditional Expectations
    4. Probability Theory: Conditional Expectation Functions
    5. Probability Theory: Asymptotic Convergence
    6. Probability Theory: Law of Large Numbers
    7. Probability Theory: Central Limit Theorem
  2. DAG: Directed Acyclic Graphs
  3. RCT: Randomized Controlled Experiments
  4. MLR: Multiple Linear Regression
  5. IVR: Instrumental Variables Regression
  6. RDD: Regression Discontinuity Designs
  7. DiD: Differences in Differences