The causalQual package provides a suite of tools for estimating causal effects when the outcome of interest is qualitative - i.e., multinomial or ordered. Standard causal inference methods such as instrumental variables (IV), regression discontinuity (RD), and difference-in-differences (DiD) are typically designed for numeric outcomes. Their direct application to qualitative outcomes leads to ill-defined estimands, rendering results arbitrary and uninterpretable.
This package implements the framework introduced in Di Francesco and Mellace (2025), shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. The methods remain compatible with conventional research designs, ensuring ease of implementation for applied researchers.
| Feature | Benefit |
|---|---|
| Avoids misleading conclusions | Conventional estimands are often undefined or depend on arbitrary outcome coding. causalQual targets interpretable and meaningful estimands. |
| Provides well-defined estimands | Instead of relying on average effects, causalQual models how treatment shifts probabilities over outcome categories. |
| Wide applicability | Supports selection-on-observables, IV, RD, and DiD. |
| Extensible and open-source | Actively developed with planned support for staggered adoption, fuzzy regression discontinuity, and more. |
To install the latest stable release from CRAN, run:
install.packages("causalQual")
Alternatively, the current development version of the package can be installed using the devtools package:
devtools::install_github("riccardo-df/causalQual")
We welcome contributions! If you encounter issues, have feature requests, or want to contribute to the package, please follow the guidelines below.
📌 Report an issue: If you encounter a bug or have a suggestion, please open an issue on GitHub: Submit an issue
📌 Contribute code: We encourage contributions via pull requests. Before submitting, please:
- Fork the repository and create a new branch.
- Ensure that your code follows the existing style and documentation conventions.
- Run tests and check for package integrity.
- Submit a pull request with a clear description of your changes.
📌 Feature requests: If you have ideas for new features or extensions, feel free to discuss them by opening an issue.
If you use causalQual in your research, please cite the corresponding paper:
Author(s). Title of Paper. arXiv, 2025