{
  "annotation_id": "6acc3b09-f977-4646-8b0d-38c41e7bf64a",
  "date_created": "2026-02-17T05:53:15.438000Z",
  "date_modified": "2026-02-17T05:53:15.438000Z",
  "file_hash": "c2d22d6b81759f36dd2d643c27efb2884b69096bbedc7e7a62f5cd524166e36e",
  "private": false,
  "record": {
    "abstract": "This paper proposes a novel approach for estimating treatment effects in panel data settings, addressing key limitations of the standard difference-in-differences (DID) approach. The standard approach relies on the parallel trends assumption, implicitly requiring that unobservable factors correlated with treatment assignment be unidimensional, time-invariant, and affect untreated potential outcomes in an additively separable manner. This paper introduces a more flexible framework that allows for multidimensional unobservables and non-additive separability, and provides sufficient conditions for identifying the average treatment effect on the treated. An empirical application to job displacement reveals substantially smaller long-run earnings losses compared to the standard DID approach, demonstrating the framework\u0027s ability to account for unobserved heterogeneity that manifests as differential outcome trajectories between treated and control groups.",
    "arxiv_id": "2601.08281",
    "authors": [
      "Shoya Ishimaru"
    ],
    "categories": [
      "econ.EM"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Estimating Treatment Effects in Panel Data Without Parallel Trends",
    "url": "https://arxiv.org/abs/2601.08281",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "24bf2ecd-217e-47c9-9406-86e2fc5a7261",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}