TROP: Triply Robust Panel Estimator

trop is a Python package implementing the Triply Robust Panel (TROP) estimator for average treatment effects in panel data. The core estimator is expressed as a weighted two-way fixed effects (TWFE) objective, with an optional low-rank regression adjustment via a nuclear-norm penalty.

Reference:

Susan Athey, Guido Imbens, Zhaonan Qu, Davide Viviano.
Triply Robust Panel Estimators
Journal of Applied Econometrics (2026)

Links

• Documentation: https://zhaonanq.github.io/TROP-Estimator/
• Source code: https://github.com/zhaonanq/TROP-Estimator

Installation

pip install trop

Quick start

import numpy as np
import trop

# Y: (N, T) outcomes; unit 0 is treated in the last `treated_periods` columns
W = np.zeros_like(Y)
W[0, -treated_periods:] = 1

# 1. Tune (lambda_unit, lambda_time, lambda_nn) by placebo cross-validation on the controls
lambdas = trop.TROP_cv_cycle(
    np.delete(Y, 0, axis=0), treated_periods,
    unit_grid=np.arange(0, 2, 0.2), time_grid=np.arange(0, 2, 0.2), nn_grid=np.arange(0.005, 0.2, 0.02),
)

# 2. Point estimate
tau = trop.TROP_TWFE_average(Y, W, [0], *lambdas, treated_periods=treated_periods)

# 3. Bootstrap standard error and 95% percentile interval (lambdas held fixed)
res = trop.bootstrap(Y, W, [0], treated_periods, lambdas=lambdas, n_bootstrap=1000, random_state=0)
print(res.estimate, res.se, res.ci)

trop.bootstrap also accepts any estimator with signature (Y, W, treated_units, treated_periods) -> float (e.g. the entries of trop.default_estimator_suite), resamples either control units only (resample="controls", the default) or treated and control units separately (resample="stratified"), and can report effects relative to the counterfactual mean (scale="relative").