mlsanityData to 14 Oct 2026

Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey

arXiv 2208.09590 ↗First posted 20 Aug 2022cs.AI cs.LGcited by 19

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Abstract (arXiv)

In many fields of scientific research and real-world applications, unbiased estimation of causal effects from non-experimental data is crucial for understanding the mechanism underlying the data and for decision-making on effective responses or interventions. A great deal of research has been conducted to address this challenging problem from different angles. For estimating causal effect in observational data, assumptions such as Markov condition, faithfulness and causal sufficiency are always made. Under the assumptions, full knowledge such as, a set of covariates or an underlying causal graph, is typically required. A practical challenge is that in many applications, no such full knowledge or only some partial knowledge is available. In recent years, research has emerged to use search strategies based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promise in tackling the practical challenge. In this survey, we review these data-driven methods on causal effect estimation for a single treatment with a single outcome of interest and focus on the challenges faced by data-driven causal effect estimation. We concisely summarise the basic concepts and theories that are essential for data-driven causal effect estimation using graphical causal modelling but are scattered around the literature. We identify and discuss the challenges faced by data-driven causal effect estimation and characterise the existing methods by their assumptions and the approaches to tackling the challenges. We analyse the strengths and limitations of the different types of methods and present an empirical evaluation to support the discussions. We hope this review will motivate more researchers to design better data-driven methods based on graphical causal modelling for the challenging problem of causal effect estimation.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 35 of its 172 references that are in the map. First posted on arXiv 20 Aug 2022.

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35 of 172 references.

References · 35

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23 more
  1. 2021An Automated Approach to Causal Inference in Discrete Settingscited by 42
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  3. 2018A Unified View of Causal and Non-causal Feature Selectioncited by 23
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Citing papers per year: 2023: 7; 2024: 10; 2025: 1; 2026: 1.

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  1. 2024FairGT: A Fairness-aware Graph Transformercited by 29
  2. 2023Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounderscited by 10
  3. 2023Conditional Instrumental Variable Regression with Representation Learning for Causal Inferencecited by 8
  4. 2023Exploring Causal Learning through Graph Neural Networks: An In-depth Reviewcited by 8
  5. 2024Mitigating Propensity Bias of Large Language Models for Recommender Systemscited by 8
  6. 2024Disentangled Representation Learning for Causal Inference with Instrumentscited by 6
  7. 2023Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledgecited by 4
  8. 2023Learning Conditional Instrumental Variable Representation for Causal Effect Estimationcited by 3
  9. 2024Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variablescited by 3
  10. 2025Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptationcited by 2
  11. 2024Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variablescited by 1
  12. 2024Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systemscited by 1
7 more
  1. 2024Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillationcited by 1
  2. 2024From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretationscited by 1
  3. 2023Causal Intervention for Measuring Confidence in Drug-Target Interaction Predictioncited by 0
  4. 2023Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inferencecited by 0
  5. 2024Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learningcited by 0
  6. 2024Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Datacited by 0
  7. 2026Disentangled Double Machine Learning for Accurate Causal Effect Estimationcited by 0