A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching

• URL: http://arxiv.org/abs/2604.27307v2
• Date: Wed, 06 May 2026 21:01:55 GMT
• Title: A Novel Computational Framework for Causal Inference: Tree-Based Discretization with ILP-Based Matching
• Abstract summary: Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data.<n>This paper proposes a novel approach that combines a tree-based discretization technique, tailored for causal inference, with an integer linear programming-based matching algorithm.<n>The resulting algorithm yields computational efficiency and less biased ATT estimates compared to state-of-the-art algorithms.
• Score: 5.526311778160277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal inference is essential for data-driven decision-making, as it aims to uncover causal relationships from observational data. However, identifying causality remains challenging due to the potential for confounding and the distinction between correlation and causation. While recent advances in causal machine learning and matching algorithms have improved estimation accuracy, these methods often face trade-offs between interpretability and computational efficiency. This paper proposes a novel approach that combines a tree-based discretization technique, tailored for causal inference, with an integer linear programming-based matching algorithm. The discretization ensures approximately linear relationships for control datasets within strata, enabling effective matching, while the optimization framework optimizes for global balance. The resulting algorithm yields computational efficiency and less biased ATT estimates compared to state-of-the-art algorithms. Empirical evaluations demonstrate the proposed method's practical advantages over existing techniques in causal inference scenarios.

Related papers

• Using Non-Lipschitz Signum-based Functions for Distributed Optimization and Machine Learning: Trade-off Between Con-vergence Rate and Optimality Gap [9.045698110081686]
  Non-Lipschitz continuous optimization algorithms have been proposed to improve the slow convergence rate of the existing linear solutions.<n>We investigate the distributed regression problem and check its convergence rate by applying both linear and non-Lipschitz signum-based functions.<n>Our results show that although adopting signum-based func-tions may give faster convergence, it results in large optimality gaps.
  arXiv  Detail & Related papers  (2026-08-02T13:06:12Z)
• Bounding Causal Effects and Counterfactuals [0.0]
  This thesis addresses challenges by systematically comparing bounding algorithms across multiple causal scenarios.<n>We implement, extend, and unify state-of-the-art methods within a common evaluation framework.<n>Our empirical study spans thousands of randomized simulations involving both discrete and continuous data-generating processes.
  arXiv  Detail & Related papers  (2025-08-19T08:13:34Z)
• Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget [55.938644481736446]
  We introduce a novel algorithm for best feasible arm identification that guarantees an exponential decay in the error probability.<n>We validate our algorithm through comprehensive empirical evaluations across various problem instances with different levels of complexity.
  arXiv  Detail & Related papers  (2025-06-03T02:56:26Z)
• A Hierarchical Federated Learning Approach for the Internet of Things [3.28418927821443]
  We present a novel federated learning solution, QHetFed, suitable for large-scale Internet of Things deployments.<n>We show that QHetFed consistently achieves high learning accuracy and significantly outperforms other hierarchical algorithms.
  arXiv  Detail & Related papers  (2024-03-03T15:40:24Z)
• Efficient Transfer Learning via Causal Bounds [8.981637739384674]
  We analyze how causal side-information accelerates online learning, and experiments on data reduction.<n>Our analysis precisely characterizes when how causal side-information accelerates online learning, and experiments on data reduction.
  arXiv  Detail & Related papers  (2023-08-07T13:24:50Z)
• Contraction-Guided Adaptive Partitioning for Reachability Analysis of Neural Network Controlled Systems [5.359060261460183]
  We present a contraction-guided adaptive partitioning algorithm for improving interval-valued reachable set estimates in a nonlinear feedback loop. By leveraging a decoupling of the neural network verification step and reachability partitioning layers, the algorithm can provide accuracy improvements for little computational cost. We report a sizable improvement in the accuracy of reachable set estimation in a fraction of the runtime as compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-07T14:43:21Z)
• From Causal Pairs to Causal Graphs [1.5469452301122175]
  Causal structure learning from observational data remains a non-trivial task. Motivated by the Cause-Effect Pair' NIPS 2013 Workshop on Causality Challenge, we take a different approach and generate a probability distribution over all possible graphs. The goal of the paper is to propose new methods based on this probabilistic information and compare their performance with traditional and state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-11-08T15:28:55Z)
• Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging [71.57324258813675]
  A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies. A neural network is employed to act as structure prior and reveal the underlying signal interdependencies. Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
  arXiv  Detail & Related papers  (2022-03-29T21:00:39Z)
• The Statistical Complexity of Interactive Decision Making [126.04974881555094]
  We provide a complexity measure, the Decision-Estimation Coefficient, that is proven to be both necessary and sufficient for sample-efficient interactive learning. A unified algorithm design principle, Estimation-to-Decisions (E2D), transforms any algorithm for supervised estimation into an online algorithm for decision making.
  arXiv  Detail & Related papers  (2021-12-27T02:53:44Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• False Correlation Reduction for Offline Reinforcement Learning [115.11954432080749]
  We propose falSe COrrelation REduction (SCORE) for offline RL, a practically effective and theoretically provable algorithm. We empirically show that SCORE achieves the SoTA performance with 3.1x acceleration on various tasks in a standard benchmark (D4RL)
  arXiv  Detail & Related papers  (2021-10-24T15:34:03Z)
• Outlier-Robust Sparse Estimation via Non-Convex Optimization [73.18654719887205]
  We explore the connection between high-dimensional statistics and non-robust optimization in the presence of sparsity constraints. We develop novel and simple optimization formulations for these problems. As a corollary, we obtain that any first-order method that efficiently converges to station yields an efficient algorithm for these tasks.
  arXiv  Detail & Related papers  (2021-09-23T17:38:24Z)
• Benchmarking Simulation-Based Inference [5.3898004059026325]
  Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods. We provide a benchmark with inference tasks and suitable performance metrics, with an initial selection of algorithms. We found that the choice of performance metric is critical, that even state-of-the-art algorithms have substantial room for improvement, and that sequential estimation improves sample efficiency.
  arXiv  Detail & Related papers  (2021-01-12T18:31:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.