Causal Inference Guide

A skill for applying quasi-experimental causal inference methods in observational research. Covers difference-in-differences, instrumental variables, regression discontinuity designs, and synthetic control methods with implementation code and diagnostic checks.

Difference-in-Differences (DiD)

Classic Two-Period DiD

import numpy as np
import pandas as pd
import statsmodels.formula.api as smf

def did_estimation(df: pd.DataFrame, outcome: str, treatment: str,
                    post: str, covariates: list[str] = None) -> dict:
    """
    Estimate a difference-in-differences model.