qte — Quantile Treatment Effects in Python

Alpha software: the API may change without notice between releases.

This is an attempt at a Python implementation of the qte R package by Brantly Callaway from here.

The main features are:

• Availability of cross-sectional quantile treatment effects estimators (simple, inverse probability weighted, outcome regression, doubly robust) and non-linear difference-in-differences estimator (changes-in-changes and quantile difference-in-differences);
• Fast:
  • as opposed to the R-package we can use highly optimized Numpy functions for computing weighted quantiles;
  • quantile regression is magnitudes faster than in other Python packages since we use highly optimized Fortran code directly (falling back to statsmodels where the extension is unavailable);
  • parallelism for bootstrapped standard errors;
  • batching and vectorization in performance critical places;
  • built natively on Polars;
• Beautiful: Graphs and tables for the console, the web, and latex powered by Altair, Great Tables and Rich.

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Installation

Requires Python 3.12 or newer.

uv add py-qte
# or
pip install py-qte

Prebuilt wheels ship for Linux (x86_64) and macOS (Intel and Apple Silicon). On those platforms quantile regression uses the bundled Fortran Frisch-Newton solver. Everywhere else — including Windows — a pure-Python wheel falls back to statsmodels for quantile regression: same results to roughly 1e-5, but slower.

Building from source additionally needs a Fortran compiler (gfortran) and BLAS/LAPACK development libraries. When they are missing, the build automatically skips the extension and installs the pure-Python fallback.

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Examples

Cross-Sectional Data

We can estimate quantile treatment (and average) treatment effects using an augmented inverse propensity score (AIPW) estimator .

from qte.cross_sectional import estimate_aipw_qte
from qte.datasets import load_lalonde

ds = load_lalonde()

res = estimate_aipw_qte(
    ds=ds,
    outcome_c="re78",
    treatment_c="treat",
    or_x_formular="age + education",
    ps_x_formular="age + education",
)

res.plot()  # Vega-Altair plot (see below)
res.tabulate()  # Great Tables output (see below)

Non-Linear Difference-in-Differences

We can estimate quantile treatment (and average) treatment effects with the changes-in-changes estimator.

from qte.nonlinear_did import (
    CounterfactualModel,
    TrtGroupConfig,
    estimate_nonlinear_did_for_panel,
)
from qte.datasets import load_mpdta

ds = load_mpdta()
res = estimate_nonlinear_did_for_panel(
    ds,
    "lemp",
    TrtGroupConfig("first.treat", 0),  # never-treated group is 0
    "year",
    "countyreal",
    qs=[0.25, 0.5, 0.75],
    counterfactual_model=CounterfactualModel.CIC,
)

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Development

Regenerate the figures above after changing the estimators or their presentation:

uv run python -m docs.readme

README_PYPI.md (what project.readme points at) is generated from this file with relative links made absolute, since PyPI can't resolve repo-relative assets. Regenerate it after editing this file:

uv run python -m docs.readme --pypi-readme-only