mlsanityData to 14 Oct 2026

Finding and Listing Front-door Adjustment Sets

arXiv 2210.05816 ↗First posted 11 Oct 2022stat.ME cs.AI cs.LGcited by 6

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Abstract (arXiv)

Identifying the effects of new interventions from data is a significant challenge found across a wide range of the empirical sciences. A well-known strategy for identifying such effects is Pearl's front-door (FD) criterion (Pearl, 1995). The definition of the FD criterion is declarative, only allowing one to decide whether a specific set satisfies the criterion. In this paper, we present algorithms for finding and enumerating possible sets satisfying the FD criterion in a given causal diagram. These results are useful in facilitating the practical applications of the FD criterion for causal effects estimation and helping scientists to select estimands with desired properties, e.g., based on cost, feasibility of measurement, or statistical power.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 7 of its 46 references that are in the map. First posted on arXiv 11 Oct 2022.

Builds on

7 of 46 references.

References · 7

  1. 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 98
  2. 2020Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variablescited by 23
  3. 2018Separators and Adjustment Sets in Causal Graphs: Complete Criteria and an Algorithmic Frameworkcited by 16
  4. 2018Causal Identification under Markov Equivalencecited by 15
  5. 2020Efficient adjustment sets in causal graphical models with hidden variablescited by 13
  6. 2021Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledgecited by 10
  7. 2022On Testability of the Front-Door Model via Verma Constraintscited by 8

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Cited by year

Citing papers per year: 2022: 1; 2023: 3; 2024: 0; 2025: 2.

6 papers.

  1. 2023Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencodercited by 17
  2. 2022Linear-Time Algorithms for Front-Door Adjustment in Causal Graphscited by 4
  3. 2023Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledgecited by 4
  4. 2023Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterioncited by 1
  5. 2025Linear-Time Primitives for Algorithm Development in Graphical Causal Inferencecited by 0
  6. 2025PIPCFR: Pseudo-outcome Imputation with Post-treatment Variables for Individual Treatment Effect Estimationcited by 0