Call for a Special Issue on "Causal Inference: past, present, and future"

The New England Journal of Statistics in Data Science (NEJSDS) invites submissions to a special issue of Call for a Special Issue on “Causal Inference: past, present, and future”.

This special issue focuses on topics related to causal inference and its interference with machine learning and empirical areas.

Manuscripts for this Special Issue should be submitted by September 1st, 2024 via the journal online submission portal Please state that your submissions are “For the Special Issue on Causal Inference: past, present, and future” in the Box of Comments to the editors. The submissions will go through a regular review process. As the guest editors for this special issue, we will handle the peer review timely and carefully.

Tentative topics include (but are not limited to):

  • historical aspects;
  • perspectives on the direction of causal inference research;
  • the role of causal inference in machine learning;
  • the role of machine learning in causal inference;
  • the role of study design for causal inference;
  • sensitivity analyses and bounds;
  • connection between survey, missing data and causal inference;
  • Bayes, O Bayes;
  • methods and applications in medicine, public health, social science, education, law, policy, etc.

We encourage submissions of papers of different types, including (but not limited to):

  • regular theoretical, methodological, or applied research;
  • commentary, controversy, or conjecture;
  • history of causal inference.

NEJSDS is proud to be a pioneer in reforming the traditional peer review process by implementing a new hybrid journal review process. In this new review process, authors have the option to supply referee reports invited by the authors, as a supplement to the traditional review reports led by the editorial board. The decisions will take into consideration both review reports initiated by the authors and the reports initiated by the editorial board. If any authors choose to provide open reviews for their articles, please mention it in the cover letter and upload the review reports (including the information of reviewers’ affiliations and email addresses) as supplementary materials when submitting the article.

If you have any questions or have a pre-submission inquiry, please contact us.
We look forward to receiving your papers in due course.

Guest Editors:

  • Peng Ding, University of California Berkeley,,
  • Fan Li, Duke University, Email:
  • Elizabeth Ogburn, Johns Hopkins University, Email:
  • Dylan Small, University of Pennsylvania, Email: