CausalEstimate 0.10.0

A library for estimates of causal effects.

CausalEstimate

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CausalEstimate is a Python library designed for causal inference, providing a suite of methods to estimate treatment effects from observational data. It includes doubly robust techniques such as Targeted Maximum Likelihood Estimation (TMLE), alongside propensity score-based methods like inverse probability weighting (IPW) and matching. The library is built for flexibility and ease of use, integrating seamlessly with pandas and supporting bootstrap-based standard error estimation and multiple estimators in one pass.

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Features

• Causal inference methods: IPW, AIPW, TMLE, Matching, etc.
• Supports multiple effect types: ATE, ATT, Risk Ratio, etc.
• Bootstrap standard error estimation and confidence intervals
• Common-support filtering and matching (greedy, optimal)
• Plotting utilities for distribution checks (e.g., propensity score overlap)

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Installation

pip install CausalEstimate

Or for local development:

git clone https://github.com/kirilklein/CausalEstimate.git
cd CausalEstimate
pip install -e .

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Usage

1) Single Estimator Usage

You can import any estimator class (e.g., IPW, AIPW, TMLE) and call compute_effect(df) directly. Columns (treatment, outcome, propensity score) are passed to the estimator in its constructor.

import numpy as np
import pandas as pd
from CausalEstimate.estimators import IPW

# Simulate data
np.random.seed(42)
n = 1000
ps = np.random.uniform(0, 1, n)          # true propensity for treatment
treatment = np.random.binomial(1, ps)    # actual treatment assignment
outcome = 2 + 0.5 * treatment + np.random.normal(0, 1, n)

df = pd.DataFrame({
    "ps": ps,
    "treatment": treatment,
    "outcome": outcome
})

# Create an IPW Estimator for ATE
ipw_estimator = IPW(
    effect_type="ATE",
    treatment_col="treatment",
    outcome_col="outcome",
    ps_col="ps",
    # optionally stabilized=True if you want stabilized IP weights
)

results = ipw_estimator.compute_effect(df)
print("IPW estimated effect:", results)

In this case, results is simply a dictionary with the effect estimate computed from a single sample run (n_bootstraps=1). When no bootstrapping is applied, the output includes the key "n_bootstraps": 0.

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2) Multi Estimator Usage

If you want to run multiple estimators (e.g., IPW, TMLE, AIPW) on the same dataset in one pass—optionally applying bootstrap or common-support filtering—you can use the MultiEstimator.

from CausalEstimate.estimators import IPW, AIPW, TMLE, MultiEstimator

ipw = IPW(
    effect_type="ATE",
    treatment_col="treatment",
    outcome_col="outcome",
    ps_col="ps"
)
aipw = AIPW(
    effect_type="ATE",
    treatment_col="treatment",
    outcome_col="outcome",
    ps_col="ps",
    probas_t1_col="predicted_outcome_treated",
    probas_t0_col="predicted_outcome_control"
)
tmle = TMLE(
    effect_type="ATE",
    treatment_col="treatment",
    outcome_col="outcome",
    ps_col="ps",
    probas_col="predicted_outcome",
    probas_t1_col="predicted_outcome_treated",
    probas_t0_col="predicted_outcome_control"
)

multi_estimator = MultiEstimator([ipw, aipw, tmle])

# Apply bootstrap (n_bootstraps > 1 triggers bootstrapping), common support, etc.
results = multi_estimator.compute_effects(
    df, 
    n_bootstraps=50,  # If n_bootstraps > 1, bootstrapping is applied.
    apply_common_support=True,
    common_support_threshold=0.05,
    return_bootstrap_samples=True  # Optionally return raw bootstrap estimates.
)
print(results)

Here, results is a dictionary with keys corresponding to each estimator's class name (e.g., "IPW", "AIPW", "TMLE"). For estimators that perform bootstrapping (i.e. when n_bootstraps > 1), the output dictionary includes:

• "effect": The mean effect across bootstrap samples.
• "std_err": The standard deviation of the bootstrap estimates.
• "CI95_lower" and "CI95_upper": The 95% confidence interval computed using the percentile method.
• "n_bootstraps": The number of bootstrap samples (e.g., 50).
• Optionally, if return_bootstrap_samples=True, a "bootstrap_samples" key with the raw bootstrap estimates (e.g., for the overall effect, treated, and untreated effects).

When no bootstrapping is performed (i.e. n_bootstraps is set to 1), "n_bootstraps" is set to 0 and the bootstrap summary keys (like "std_err", "CI95_lower", "CI95_upper") may not be present.

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3) Matching

The library supports both optimal and greedy (a.k.a. eager) matching. For example:

import pandas as pd
import numpy as np
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 (with 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"
)
print("Optimal Matching Results:")
print(matched_optimal)

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

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

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4) Plotting

CausalEstimate provides basic plotting utilities to visualize distributions of propensity scores or predicted outcome probabilities across treatment vs. control.

Example: Propensity Score Distribution

📌 Generated from this notebook

import matplotlib.pyplot as plt
from CausalEstimate.vis.plotting import plot_propensity_score_dist, plot_outcome_proba_dist

# Suppose df has columns "ps", "treatment", and "predicted_outcome"
fig, ax = plot_propensity_score_dist(df, ps_col="ps", treatment_col="treatment")
plt.show()

fig, ax = plot_outcome_proba_dist(df, outcome_proba_col="predicted_outcome", treatment_col="treatment")
plt.show()

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Development

See CONTRIBUTING.md for details on setting up a dev environment, running tests, and contributing to this project.

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License

CausalEstimate is licensed under the MIT License. See LICENSE for more details.

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Contact

• GitHub: kirilklein
• Email: [e-mail]

Please open issues or pull requests if you find any bugs or want to propose enhancements.

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Citation

If you use CausalEstimate in your research, please cite it using the following BibTeX entry:

@software{causalestimate,
  author = {Kiril Klein, ...},
  title = {CausalEstimate: A Python Library for Causal Inference},
  year = {2024},
  url = {https://github.com/kirilklein/CausalEstimate},
  version = {X.Y.Z},
  note = {GitHub repository}
}