---
res:
  bibo_abstract:
  - "In many scientific experiments, the data annotating cost constraints the pace\r\nfor
    testing novel hypotheses. Yet, modern machine learning pipelines offer a\r\npromising
    solution—provided their predictions yield correct conclusions. We\r\nfocus on
    Prediction-Powered Causal Inferences (PPCI), i.e., estimating the\r\ntreatment
    effect in an unlabeled target experiment, relying on training data with\r\nthe
    same outcome annotated but potentially different treatment or effect modifiers.\r\nWe
    first show that conditional calibration guarantees valid PPCI at population\r\nlevel.
    Then, we introduce a sufficient representation constraint transferring validity\r\nacross
    experiments, which we propose to enforce in practice in Deconfounded\r\nEmpirical
    Risk Minimization, our new model-agnostic training objective. We\r\nvalidate our
    method on synthetic and real-world scientific data, solving impossible\r\nproblem
    instances for Empirical Risk Minimization even with standard invariance\r\nconstraints.
    In particular, for the first time, we achieve valid causal inference\r\non a scientific
    experiment with complex recording and no human annotations,\r\nfine-tuning a foundational
    model on our similar annotated experiment.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Riccardo
      foaf_name: Cadei, Riccardo
      foaf_surname: Cadei
      foaf_workInfoHomepage: http://www.librecat.org/personId=0fa8b76f-72f0-11ef-b75a-a5da96e5ad6b
  - foaf_Person:
      foaf_givenName: Ilker
      foaf_name: Demirel, Ilker
      foaf_surname: Demirel
  - foaf_Person:
      foaf_givenName: Piersilvio
      foaf_name: De Bartolomeis, Piersilvio
      foaf_surname: De Bartolomeis
  - foaf_Person:
      foaf_givenName: Lukas
      foaf_name: Lindorfer, Lukas
      foaf_surname: Lindorfer
      foaf_workInfoHomepage: http://www.librecat.org/personId=85f0e6d3-06b3-11ec-8982-8c5049fa4455
  - foaf_Person:
      foaf_givenName: Sylvia
      foaf_name: Cremer, Sylvia
      foaf_surname: Cremer
      foaf_workInfoHomepage: http://www.librecat.org/personId=2F64EC8C-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0002-2193-3868
  - foaf_Person:
      foaf_givenName: Cordelia
      foaf_name: Schmid, Cordelia
      foaf_surname: Schmid
  - foaf_Person:
      foaf_givenName: Francesco
      foaf_name: Locatello, Francesco
      foaf_surname: Locatello
      foaf_workInfoHomepage: http://www.librecat.org/personId=26cfd52f-2483-11ee-8040-88983bcc06d4
    orcid: 0000-0002-4850-0683
  bibo_doi: 10.52202/085713-2479
  bibo_volume: 38
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1049-5258
  - http://id.crossref.org/issn/9798331338275
  dct_language: eng
  dct_publisher: Neural Information Processing Systems Foundation@
  dct_title: Prediction-Powered Causal Inferences@
...