# idid

`idid` implements instrumented difference-in-differences (IDiD) estimators for
cohort-time local average treatment effects on the treated, $LATT(e,t)$. The
package covers both balanced panel data and repeated cross-sections, and
includes the doubly robust (DR) and double machine learning (DML) estimators
developed in [Raaschou-Pedersen (2026)](https://arxiv.org/abs/2605.03699). It also provides tools for aggregating cohort-time effects
into event-study and summary parameters, together with plotting utilities for
inspecting the aggregated effects.

Paper: [Raaschou-Pedersen (2026)](https://arxiv.org/abs/2605.03699)

Start with the [Quickstart](quickstart) for the basic estimation workflow.

```{toctree}
:maxdepth: 2
:caption: User Guide

quickstart
aggregation
examples
theory
README
```

```{toctree}
:maxdepth: 2
:caption: API Reference

apidocs/index
types
```

```{toctree}
:maxdepth: 1
:caption: Advanced API

apidocs/idid/idid.dridid
apidocs/idid/idid.dmlidid
```

## Related Package

[`csa-py`](https://github.com/jsr-p/csa-py) is the sibling package for
the estimators of Callaway and Sant'Anna (2021).