causal-toolkit-tc 0.1.4
A Python package for causal inference methods including ATE estimation, propensity score methods, and meta-learners
License: MIT
Requires Python: >=3.8
Keywords: causal inference, statistics, machine learning, treatment effects

# causal_toolkit_tc
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A Python package for causal inference methods including ATE estimation, propensity scores, and meta-learners.

---

## 📦 Features
- **RCT Methods**: `calculate_ate_ci()`, `calculate_ate_pvalue()`
- **Propensity Score**: `ipw()`, `doubly_robust()`
- **Meta-Learners**: `s_learner_discrete()`, `t_learner_discrete()`, `x_learner_discrete()`, `double_ml_cate()`

---

## ✅ Installation

**For Users:**
```bash
pip install causal_toolkit_tc
```

**For Contributors:**
```bash
git clone https://github.com/tc-git-1/causal_toolkit_tc.git
cd causal_toolkit_tc
pip install -e ".[dev]"
```

---

## 🚀 Quick Start

Here are examples for every function in the package.

```python
import pandas as pd
import numpy as np
from causal_toolkit_tc.rct import calculate_ate_ci, calculate_ate_pvalue
from causal_toolkit_tc.propensity import ipw, doubly_robust
from causal_toolkit_tc.meta_learners import (
    s_learner_discrete, t_learner_discrete, x_learner_discrete, double_ml_cate
)

# Generate Binary Treatment Data (for RCT, Propensity, S/T/X Learners)
np.random.seed(42)
n = 1000
df = pd.DataFrame({
    'age': np.random.normal(30, 10, n),
    'income': np.random.normal(50000, 10000, n),
    'T': np.random.binomial(1, 0.5, n), # Binary Treatment
})

# Outcome depends on T and confounders
# True ATE = 2.0
df['Y'] = 2*df['T'] + 0.05*df['age'] + 0.001*df['income'] + np.random.normal(0, 1, n)

# Generate Continuous Treatment Data (for Double ML)
df_cont = df.copy()
df_cont['T_cont'] = np.random.normal(5, 2, n) # Continuous Treatment
# True ATE = 1.5
df_cont['Y_cont'] = 1.5*df_cont['T_cont'] + 0.1*df_cont['age'] + np.random.normal(0, 1, n)

# --- EXECUTION ---

print("--- RCT Methods ---")
print(f"True ATE: 2.0")

# Estimate ATE and Confidence Interval
ate, lower, upper = calculate_ate_ci(df)
print(f"ATE (CI): {ate:.3f} [{lower:.3f}, {upper:.3f}]")

# Estimate ATE and P-Value
ate, t_stat, p_val = calculate_ate_pvalue(df)
print(f"ATE (p-val): {ate:.3f} (p={p_val:.3f})")

print("\n--- Propensity Score Methods ---")
print(f"True ATE: 2.0")

# Define the formula for the propensity score model (T ~ age + income)
ps_formula = "age + income"

# Inverse Propensity Weighting
ate_ipw = ipw(df, ps_formula, 'T', 'Y')
print(f"IPW ATE: {ate_ipw:.3f}")

# Doubly Robust Estimator (IPW + Outcome Regression)
ate_dr = doubly_robust(df, ps_formula, 'T', 'Y')
print(f"Doubly Robust ATE: {ate_dr:.3f}")

print("\n--- Meta-Learners ---")

# Split data for training and inference
train = df.iloc[:800].copy()
test = df.iloc[800:].copy()
covariates = ['age', 'income']

# A. Discrete Learners (Binary Treatment)
# ---------------------------------------
print("A. Discrete Learners (True ATE: 2.0)")

# S-Learner (Single Model)
res_s = s_learner_discrete(train, test, covariates, 'T', 'Y')
print(f"S-Learner CATE (head): \n{res_s['cate'].head(3).values}")
print(f"S-Learner Average CATE: {res_s['cate'].mean():.3f}")
print("")

# T-Learner (Two Models)
res_t = t_learner_discrete(train, test, covariates, 'T', 'Y')
print(f"T-Learner CATE (head): \n{res_t['cate'].head(3).values}")
print(f"T-Learner Average CATE: {res_t['cate'].mean():.3f}")
print("")

# X-Learner (Cross Learners)
res_x = x_learner_discrete(train, test, covariates, 'T', 'Y')
print(f"X-Learner CATE (head): \n{res_x['cate'].head(3).values}")
print(f"X-Learner Average CATE: {res_x['cate'].mean():.3f}")
print("")

# B. Continuous Learner (Continuous Treatment)
# --------------------------------------------
print("\nB. Continuous Learner (True ATE: 1.5)")
train_c = df_cont.iloc[:800].copy()
test_c = df_cont.iloc[800:].copy()

# Double ML (Residualization)
res_dml = double_ml_cate(train_c, test_c, covariates, 'T_cont', 'Y_cont')
print(f"Double ML CATE (head): \n{res_dml['cate'].head(3).values}")
print(f"Double ML Average CATE: {res_dml['cate'].mean():.3f}")
```

---

## 📚 API Reference

### 1. Randomized Controlled Trials (`causal_toolkit_tc.rct`)

#### `calculate_ate_ci(data)`
Calculates the Average Treatment Effect (ATE) and a 95% confidence interval using a simple difference-in-means estimator.

- **Assumptions:**
  - Treatment is **randomly assigned** (no confounding).  
  - Treatment is **binary** (0/1).  
  - Outcomes are **independent** across units.  

- **Input:** DataFrame with columns:
  - `T` — binary treatment indicator
  - `Y` — outcome
- **Returns:** `(ate, ci_lower, ci_upper)`

#### `calculate_ate_pvalue(data)`
Computes the ATE along with the t-statistic and p-value for testing the null hypothesis that **ATE = 0**.

- **Assumptions:** Same as `calculate_ate_ci`
- **Input:** DataFrame with columns `T` (treatment) and `Y` (outcome)
- **Returns:** `(ate, t_stat, p_value)`

---

### 2. Propensity Score Methods (`causal_toolkit_tc.propensity`)

#### `ipw(df, ps_formula, T, Y)`
Estimates the ATE using **Inverse Propensity Weighting (IPW)**, where the propensity score is estimated using a logistic model.

- **Assumptions:**
  - **Ignorability / unconfoundedness:** All confounders affecting treatment and outcome are observed.  
  - **Positivity / overlap:** Every unit has a non-zero probability of receiving either treatment.  
  - Correct specification of the propensity score model improves accuracy.

- **Input:**
  - `df` — DataFrame
  - `ps_formula` — RHS formula (e.g., `"age + income"`)
  - `T` — treatment column name
  - `Y` — outcome column name
- **Returns:** Estimated ATE (float)

#### `doubly_robust(df, ps_formula, T, Y)`
Estimates the ATE using a **Doubly Robust estimator**, combining IPW with an outcome regression model.

- **Assumptions:**
  - Same as IPW.
  - Consistency holds if **either** the propensity model or the outcome regression model is correctly specified.

- **Input:** Same as above
- **Returns:** Estimated ATE (float)

---

### 3. Meta-Learners (`causal_toolkit_tc.meta_learners`)
Machine-learning estimators for heterogeneous treatment effects (CATE). 

**Assumptions for Meta-Learners:**
- Treatment is **unconfounded** given observed covariates.  
- There is sufficient **overlap**: for each combination of covariates, there is a non-zero probability of receiving either treatment.  
- Models are **correctly specified or flexible enough** to capture treatment and outcome relationships.  
- Functions with the suffix `_discrete` are designed specifically for **binary treatments** (0 or 1).

#### `s_learner_discrete(train, test, X, T, y)`
**S-Learner:** Fits a single predictive model that includes treatment as a feature.

- **Returns:** A copy of the `test` DataFrame with a new column `cate` (estimated conditional treatment effect).

#### `t_learner_discrete(train, test, X, T, y)`
**T-Learner:** Trains two separate models—one for treated units and one for controls—and computes CATE as the difference in predicted outcomes.

- **Returns:** A copy of the `test` DataFrame with a new column `cate`.

#### `x_learner_discrete(train, test, X, T, y)`
**X-Learner:** A multi-stage procedure designed for **unbalanced treatment groups** (when either treatment or control groups are much larger). It imputes missing potential outcomes and learns treatment effects using weighted models.

- **Returns:** A copy of the `test` DataFrame with a new column `cate`.

#### `double_ml_cate(train, test, X, T, y)`
**Double Machine Learning (DML):** Uses orthogonalization and cross-fitting to isolate the treatment effect. Works for both **binary and continuous** treatments and is appropriate in settings with high-dimensional confounding.

- **Returns:** A copy of the `test` DataFrame with a new column `cate`.