mlsanityData to 7 Oct 2026

Obtaining Causal Information by Merging Datasets with MAXENT

arXiv 2107.07640 ↗First posted 15 Jul 2021stat.ME stat.MLcited by 9

Not on the map. Why?

Abstract (arXiv)

The investigation of the question "which treatment has a causal effect on a target variable?" is of particular relevance in a large number of scientific disciplines. This challenging task becomes even more difficult if not all treatment variables were or even cannot be observed jointly with the target variable. Another similarly important and challenging task is to quantify the causal influence of a treatment on a target in the presence of confounders. In this paper, we discuss how causal knowledge can be obtained without having observed all variables jointly, but by merging the statistical information from different datasets. We show how the maximum entropy principle can be used to identify edges among random variables when assuming causal sufficiency and an extended version of faithfulness, and when only subsets of the variables have been observed jointly.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 16 of its 86 references that are in the map. First posted on arXiv 15 Jul 2021.

Builds on

16 of 86 references.

References · 16

  1. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  2. 2018The Blessings of Multiple Causescited by 103
  3. 2017Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discoverycited by 76
  4. 2018Removing Hidden Confounding by Experimental Groundingcited by 64
  5. 2018Graphical Models for Processing Missing Datacited by 57
  6. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  7. 2013Strong Completeness and Faithfulness in Bayesian Networkscited by 44
  8. 2015backShift: Learning causal cyclic graphs from unknown shift interventionscited by 37
  9. 2018Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounderscited by 35
  10. 2016Entropic Causal Inferencecited by 27
  11. 2013Cyclic Causal Discovery from Continuous Equilibrium Datacited by 21
  12. 2013Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedurecited by 18
4 more
  1. 2021Entropic Causal Inference: Identifiability and Finite Sample Resultscited by 12
  2. 2013Testing Identifiability of Causal Effectscited by 8
  3. 2021Causal versions of Maximum Entropy and Principle of Insufficient Reasoncited by 7
  4. 2014Estimating causal structure using conditional DAG modelscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 2; 2025: 1; 2026: 1.

9 papers.

  1. 2023Multi-View Causal Representation Learning with Partial Observabilitycited by 72
  2. 2023Scalable Causal Discovery with Score Matchingcited by 42
  3. 2022Causal Inference Through the Structural Causal Marginal Problemcited by 22
  4. 2023Out-of-Variable Generalization for Discriminative Modelscited by 4
  5. 2023Bounding probabilities of causation through the causal marginal problemcited by 3
  6. 2024Estimating Joint Interventional Distributions from Marginal Interventional Datacited by 2
  7. 2024Cross-validating causal discovery via Leave-One-Variable-Outcited by 2
  8. 2025Learning Joint Interventional Effects from Single-Variable Interventions in Additive Modelscited by 0
  9. 2026Multi-Level Causal Embeddingscited by 0