Propensity score matching

CausalEstimate supports both optimal and greedy (a.k.a. eager) propensity-score matching.

import pandas as pd
from CausalEstimate.matching import match_optimal, match_eager

df = pd.DataFrame({
    "PID": [101, 102, 103, 202, 203, 204],
    "treatment": [1, 1, 1, 0, 0, 0],
    "ps": [0.30, 0.35, 0.90, 0.31, 0.34, 0.85],
})

# Optimal matching (caliper=0.05, 1 control per treated)
matched_optimal = match_optimal(
    df, n_controls=1, caliper=0.05,
    treatment_col="treatment", ps_col="ps", pid_col="PID",
)

# Eager (greedy) matching
matched_eager = match_eager(
    df, caliper=0.05,
    treatment_col="treatment", ps_col="ps", pid_col="PID",
)

Both functions return a DataFrame of matched pairs (or sets) with columns like [treated_pid, control_pid, distance].

Optimal matching minimizes the total propensity-score distance across all pairs; eager matching assigns the nearest available control to each treated unit in turn (faster, but can be globally suboptimal).

Interpreting matched estimates

With a caliper, unmatched units are dropped, so the matched population is strictly neither the full population (ATE) nor the treated population (ATT). The Matching estimator reports it as ATE with this caveat.

See the API Reference for full signatures.