{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-treatment-effects-in-panels-with","title":"Learning Treatment Effects in Panels with General Intervention Patterns","arxiv_id":"2106.02780","date":"2021-06-05","proceeding":"NeurIPS 2021 12","authors":["Vivek F. Farias","Andrew A. Li","Tianyi Peng"],"abstract":"The problem of causal inference with panel data is a central econometric question. The following is a fundamental version of this problem: Let $M^*$ be a low rank matrix and $E$ be a zero-mean noise matrix. For a `treatment' matrix $Z$ with entries in $\\{0,1\\}$ we observe the matrix $O$ with entries $O_{ij} := M^*_{ij} + E_{ij} + \\mathcal{T}_{ij} Z_{ij}$ where $\\mathcal{T}_{ij} $ are unknown, heterogenous treatment effects. The problem requires we estimate the average treatment effect $\\tau^* := \\sum_{ij} \\mathcal{T}_{ij} Z_{ij} / \\sum_{ij} Z_{ij}$. The synthetic control paradigm provides an approach to estimating $\\tau^*$ when $Z$ places support on a single row. This paper extends that framework to allow rate-optimal recovery of $\\tau^*$ for general $Z$, thus broadly expanding its applicability. Our guarantees are the first of their type in this general setting. Computational experiments on synthetic and real-world data show a substantial advantage over competing estimators.","url_abs":"https://arxiv.org/abs/2106.02780v2","url_pdf":"https://arxiv.org/pdf/2106.02780v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-treatment-effects-in-panels-with","repo_url":"https://github.com/TianyiPeng/Causal-Inference-Code","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"causal-inference","task_name":"Causal Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2106.02780","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}