Data to 14 Oct 2026

Differentiable Multi-Target Causal Bayesian Experimental Design

arXiv 2302.10607 ↗First posted 21 Feb 2023cs.LG cs.AI stat.MEcited by 15

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Abstract (arXiv)

We introduce a gradient-based approach for the problem of Bayesian optimal experimental design to learn causal models in a batch setting -- a critical component for causal discovery from finite data where interventions can be costly or risky. Existing methods rely on greedy approximations to construct a batch of experiments while using black-box methods to optimize over a single target-state pair to intervene with. In this work, we completely dispose of the black-box optimization techniques and greedy heuristics and instead propose a conceptually simple end-to-end gradient-based optimization procedure to acquire a set of optimal intervention target-state pairs. Such a procedure enables parameterization of the design space to efficiently optimize over a batch of multi-target-state interventions, a setting which has hitherto not been explored due to its complexity. We demonstrate that our proposed method outperforms baselines and existing acquisition strategies in both single-target and multi-target settings across a number of synthetic datasets.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 36 of its 66 references that are in the map. First posted on arXiv 21 Feb 2023.

Builds on

36 of 66 references.

References · 36

2014Adam: A Method for Stochastic Optimizationcited by 37,240
2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
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2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,148
2022Bayesian Structure Learning with Generative Flow Networkscited by 153
2017Invariant Causal Prediction for Nonlinear Modelscited by 140
2019Variational Bayesian Optimal Experimental Designcited by 116
2021Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designcited by 106
2021DiBS: Differentiable Bayesian Structure Learningcited by 106
2017On Nesting Monte Carlo Estimatorscited by 83
2019Reparameterizable Subset Sampling via Continuous Relaxationscited by 70

24 more

2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
2021Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoodscited by 61
2019A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experimentscited by 58
2020Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimationcited by 56
2019ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discoverycited by 52
2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
2022Optimizing Sequential Experimental Design with Deep Reinforcement Learningcited by 46
2017Cost-Optimal Learning of Causal Graphscited by 44
2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
2021Learning Neural Causal Models with Active Interventionscited by 40
2017Budgeted Experiment Design for Causal Structure Learningcited by 38
2020Active Invariant Causal Prediction: Experiment Selection through Stabilitycited by 37
2022Active Bayesian Causal Inferencecited by 35
2018Efficient Bayesian Experimental Design for Implicit Modelscited by 34
2013Data Analysis with Bayesian Networks: A Bootstrap Approachcited by 32
2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30
2021Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Boundscited by 27
2018Experimental Design for Cost-Aware Learning of Causal Graphscited by 25
2019Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networkscited by 18
2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
2022Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Modelscited by 16
2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Modelscited by 7
2022Trust Your nabla: Gradient-based Intervention Targeting for Causal Discoverycited by 5
2022Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisationcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 9; 2025: 2; 2026: 3.

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2023BayesDAG: Gradient-Based Posterior Inference for Causal Discoverycited by 33
2024A Meta-Learning Approach to Bayesian Causal Discoverycited by 22
2024Amortized Active Causal Induction with Deep Reinforcement Learningcited by 10
2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
2024ProDAG: Projected Variational Inference for Directed Acyclic Graphscited by 4
2024Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experimentscited by 3
2024Causal Discovery under Off-Target Interventionscited by 2
2024Is merging worth it? Securely evaluating the information gain for causal dataset acquisitioncited by 2
2025Goal-Oriented Sequential Bayesian Experimental Design for Causal Learningcited by 2
2025Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphscited by 2
2026Can Neural Networks Learn by Experimenting on Themselves? Self-Interventional Learning from Functional Consequences to Predictive Self-Knowledgecited by 2
2024Scalable Variational Causal Discovery Unconstrained by Acyclicitycited by 1

3 more

2024Graph Agnostic Causal Bayesian Optimisationcited by 1
2026Adversarial Causal Intervention Falsificationcited by 1
2026Causal Preference Elicitationcited by 0