Causal Inference for Data Science

Data Professionals interested in building on their foundational statistics and Python knowledge to better understand and evaluate causal effects.

Probability

• Familiarity with topics such as random variables, probability distributions, expectation and variance, conditional probability.

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Inferential Statistics

• Familiarity with topics such as Central Limit Theorem, hypothesis testing, confidence intervals, linear and logistic regression for statistical inference (eg., interpreting coefficients from a model).

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Python & Data Analysis

• Familiarity with using Python as part of a Data Science workflow (Pandas, NumPy, Matplotlib, Seaborn).
• Basic data cleaning and preparation skills.

By the end of the course, you will know how to identify necessary assumptions, estimand of interest, and potential causes of bias for causal inference. You will estimate causal effect using propensity-score-based matching and weighting methods, identify when difference in differences is appropriate, and use it to estimate causal effects for observational data.