---
res:
  bibo_abstract:
  - "Machine Learning and AI have the potential to transform data-driven scientific
    discovery,\r\nenabling accurate predictions for several scientific phenomena.
    As many scientific questions\r\nare inherently causal, this paper looks at the
    causal inference task of treatment effect\r\nestimation, where the outcome of
    interest is recorded in high-dimensional observations\r\nin a Randomized Controlled
    Trial (RCT). Despite being the simplest possible causal setting\r\nand a perfect
    fit for deep learning, we theoretically find that many common choices in the\r\nliterature
    may lead to biased estimates. To test the practical impact of these considerations,\r\nwe
    recorded ISTAnt, the first real-world benchmark for causal inference downstream
    tasks\r\non high-dimensional observations as an RCT studying how garden ants (Lasius
    neglectus)\r\nrespond to microparticles applied onto their colony members by hygienic
    grooming.\r\nComparing 6 480 models fine-tuned from state-of-the-art visual backbones,
    we find that\r\nthe sampling and modeling choices significantly affect the accuracy
    of the causal estimate,\r\nand that classification accuracy is not a proxy thereof.
    We further validated the analysis,\r\nrepeating it on a synthetically generated
    visual data set controlling the causal model. Our\r\nresults suggest that future
    benchmarks should carefully consider real downstream scientific\r\nquestions,
    especially causal ones. Further, we highlight guidelines for representation\r\nlearning
    methods to help answer causal questions in the sciences.\r\nCode: https://github.com/CausalLearningAI/ISTAnt\r\nData:
    https://doi.org/10.6084/m9.figshare.26484934.v2@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: 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: 10.52202/079017-0820
  bibo_volume: 37
  dct_date: 2024^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/1049-5258
  dct_language: eng
  dct_publisher: Neural Information Processing Systems Foundation@
  dct_title: Smoke and mirrors in causal downstream tasks@
...