did-had 0.2.0
Difference-in-Differences with Heterogeneous Adoption Design (HAD) estimator
License: MIT
Requires Python: >=3.8
Keywords: difference-in-differences, causal inference, econometrics, heterogeneous treatment effects, panel data, did, had, nprobust, local polynomial regression

# did-had

A Python implementation of the **Heterogeneous Adoption Design (HAD)** estimator for difference-in-differences analysis from [de Chaisemartin et al. (2025)](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4284811).

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## Overview

The `did-had` package implements the HAD estimator for settings where:

- All groups receive treatment but with **different intensities** (no pure control group)
- Treatment adoption occurs at the **same time** for all groups, but doses vary
- Some groups have treatment doses **close to zero**, serving as "quasi-untreated" groups

This is a Python port of the official Stata package [`did_had`](https://github.com/chaisemartinPackages/did_had), producing **numerically identical results**.

## Installation

```bash
pip install did-had
```

For plotting support:
```bash
pip install did-had[plot]
```

## Quick Start

```python
import pandas as pd
from did_had import DidHad

# Load your panel data
df = pd.read_stata("tutorial_data.dta")

# Create and fit the model
model = DidHad(kernel="tri")
results = model.fit(
    df=df,
    outcome="y",        # outcome variable
    group="g",          # group identifier
    time="t",           # time period
    treatment="d",      # treatment dose
    effects=5,          # post-treatment periods
    placebo=4           # pre-treatment placebos
)

# View results
print(results)

# Get ATT
print(f"Average Treatment Effect: {results.att():.4f}")

# Save results
model.save_results("results.csv", format="csv")
```

## Example Output

```
===========================================================================
DID-HAD Estimation Results
===========================================================================
Number of groups: 1,000
Number of periods: 10
Adoption period (F): 6.0
Kernel: tri
Confidence level: 95%

---------------------------------------------------------------------------
                          Effect Estimates                      QUG* Test
         --------------------------------------------------- ---------------
          Estimate       SE     LB.CI     UB.CI     N      BW    N.BW        T    p.val
Effect_1   4.28198  0.55814   2.71751   4.90538 1,000 0.36133    371  3.96182  0.20154
Effect_2   3.59260  0.66675   2.02563   4.63925 1,000 0.27104    282  3.96182  0.20154
Effect_3   4.25466  0.67544   2.71354   5.36123 1,000 0.31407    324  3.96182  0.20154
...
```

## Features

- **Exact replication** of Stata's `did_had` command
- **Bias-corrected inference** using local polynomial regression (lprobust-style)
- **Quasi-untreated group tests** to validate the estimation strategy
- **Event-study plots** for visualization
- **Multiple output formats**: CSV, Stata, Excel, pickle

## API Reference

### `DidHad` Class

```python
DidHad(kernel="epa", alpha=0.05, nnmatch=3)
```

**Parameters:**
- `kernel`: Kernel function - `"epa"` (Epanechnikov), `"tri"` (triangular), `"uni"` (uniform), `"gau"` (Gaussian)
- `alpha`: Significance level for confidence intervals (default: 0.05 for 95% CI)
- `nnmatch`: Number of nearest neighbors for variance estimation

### `fit()` Method

```python
results = model.fit(
    df,                      # Panel DataFrame
    outcome,                 # Outcome variable name
    group,                   # Group identifier name
    time,                    # Time period name
    treatment,               # Treatment dose name
    effects=1,               # Number of post-treatment periods
    placebo=0,               # Number of pre-treatment placebos
    dynamic=False,           # Use cumulative treatment dose
    bandwidth=None,          # Global bandwidth (or Silverman if None)
    bandwidth_effect=None,   # Effect-specific bandwidths (dict or scalar)
    bandwidth_placebo=None   # Placebo-specific bandwidths (dict or scalar)
)
```

### `DidHadResults` Object

```python
results.summary()           # Formatted summary string
results.to_dataframe()      # Full results as DataFrame
results.att()               # Average treatment effect on treated
results.effects             # DataFrame of effect estimates
results.placebos            # DataFrame of placebo estimates
```

### Plotting

```python
# Basic plot
model.plot()

# Customized plot
model.plot(
    figsize=(10, 6),
    title="My Event Study",
    xlabel="Periods since treatment",
    ylabel="Treatment Effect",
    show_ci=True
)
```

### Saving Results

```python
model.save_results("results.csv", format="csv")
model.save_results("results.dta", format="stata")
model.save_results("results.xlsx", format="excel")
model.save_results("results.pkl", format="pickle")
```

## Comparison with Stata

This package produces **numerically identical** results to the official Stata `did_had` command when using the same bandwidths:

| Estimate | Python | Stata | Match |
|----------|--------|-------|-------|
| Effect_1 | 4.28198 | 4.28198 | Yes |
| Effect_2 | 3.59260 | 3.59260 | Yes |
| Effect_3 | 4.25466 | 4.25466 | Yes |
| ... | ... | ... | ... |

## Requirements

- Python >= 3.8
- NumPy >= 1.20.0
- Pandas >= 1.3.0
- Matplotlib >= 3.4.0 (optional, for plotting)

## References

- de Chaisemartin, C., D'Haultfoeuille, X., Pasquier, F., & Vazquez-Bare, G. (2025). "Difference-in-Differences Estimators for Treatments Continuously Distributed at Every Period". [SSRN](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4284811)

- Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2019). "nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference". Journal of Statistical Software.

## License

MIT License. See [LICENSE](LICENSE) for details.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on [GitHub](https://github.com/anzonyquispe/did-had).