pycausal-inference-joshlim 0.1.1

A Python package for causal inference methods including ATE estimation, propensity score methods, and meta-learners

Causal Inference Python Package - Josh Lim

This package provides key causal inference methods. These methods include ATE estimation from randomized experiments, propensity score methods, and meta-learners.

Installation

Install from PyPI:

pip install pycausal-inference-joshlim

Or install in editable mode for development:

git clone https://github.com/jhl126/pycausal-inference-joshlim.git
cd pycausal-inference-joshlim
uv pip install -e .

Usage

Import functions with the following code:

from pycausal_inference_joshlim import calculate_ate_ci, calculate_ate_pvalue
from pycausal_inference_joshlim import ipw, doubly_robust
from pycausal_inference_joshlim import s_learner_discrete, t_learner_discrete, x_learner_discrete, double_ml_cate

API Documentation

RCT Module

• calculate_ate_ci(data) - Calculates the average treatment effect (ATE) and confidence interval from randomized experiment data
• calculate_ate_pvalue(data) - Calculates the p-value for the ATE estimate

Propensity Score Module

• ipw(data) - Estimates the ATE using inverse probability weighting
• doubly_robust(data) - Estimates the ATE using the doubly robust estimator

Meta-Learners Module

• s_learner_discrete(data) - Estimates heterogeneous treatment effects using the S-Learner approach
• t_learner_discrete(data) - Estimates heterogeneous treatment effects using the T-Learner approach
• x_learner_discrete(data) - Estimates heterogeneous treatment effects using the X-Learner approach
• double_ml_cate(data) - Estimates heterogeneous treatment effects using Double ML