README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# fect

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**R** package for implementing counterfactual estimators, also known as imputation estimators, in panel fixed-effect settings. Suitable for causal panel analysis with binary treatments under (hypothetically) baseline randomization. It allows a treatment to switch on and off and limited carryover effects. It supports two-way fixed effects, linear factor models, and the matrix completion method. 

Starting from v.2.0.0, all **gsynth** functionalities have been merged into **fect**.

**Source Code:** [GitHub](https://github.com/xuyiqing/fect) 

**User Manual:** [Quarto Book](https://yiqingxu.org/packages/fect/) 

**Main References:** 

Xu, Yiqing (2017). [Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models](https://www.cambridge.org/core/journals/political-analysis/article/generalized-synthetic-control-method-causal-inference-with-interactive-fixed-effects-models/B63A8BD7C239DD4141C67DA10CD0E4F3). *Political Analysis* 25 (1): 57--76.

Liu, Licheng, Ye Wang, Yiqing Xu (2024). [A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data](https://yiqingxu.org/papers/english/2022_fect/LWX2022.pdf). *American Journal of Political Science*, 68 (1): 160--76.

Chiu, Albert, Xingchen Lan, Ziyi Liu, and Yiqing Xu. (2025). [Causal Panel Analysis Under Parallel Trends: Lessons from a Large Reanalysis Study](https://www.cambridge.org/core/journals/american-political-science-review/article/causal-panel-analysis-under-parallel-trends-lessons-from-a-large-reanalysis-study/219275E0CE901F099F2CFFBA07079243). *American Political Science Review*, First View.

**Report bugs:** Please report any bugs by submitting an issue on [GitHub](https://github.com/xuyiqing/fect/issues) or emailing me (yiqingxu [at] stanford.edu). We'd really appreciate it if you can include your minimally replicable code & data file and a **panelView** treatment status plot. Your feedback is highly valued!