Author name cluster

Mohammad Ali Javidian

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

AAMAS Conference 2026 Conference Paper

Causal Domain Adaptation: An Information Bottleneck Approach

Mohammad Ali Javidian

We address a common causal domain adaptation scenario in which the target variable is observed in the source domain but completely unobserved in the target domain. Our goal is to impute the missing target values in the target domain using the remaining observed variables, under various shifts. We cast this problem as learning a compact representation that is stable across mechanisms: retaining information needed to predict the target while filtering out spurious variation. For linear Gaussian causal models, we derive a closed-form Gaussian Information Bottleneck solution that reduces to a canonical-correlation-style projection and can incorporate DAG-aware structure when desired. For nonlinear or non- Gaussian settings, we propose a Variational Information Bottleneck encoder–predictor that scales to high-dimensional data, can be trained on the source domain, and deployed zero-shot in the target domain. Experiments on synthetic and real datasets show that our method consistently produces accurate imputations, enabling practical deployment in high-dimensional causal models and providing a unified, lightweight toolkit for causal domain adaptation.

PDF

AAMAS Conference 2026 Conference Paper

Extending Multi-source Bayesian Optimization With Causality Principles

Luuk Jacobs
Mohammad Ali Javidian

Multi-Source Bayesian Optimization (MSBO) serves as a variant of the traditional Bayesian Optimization (BO) framework applicable to situations involving optimization of an objective black-box function over multiple information sources such as simulations, surrogate models, or real-world experiments. However, traditional MSBO assumes the input variables of the objective function to be independent and identically distributed, limiting its effectiveness in scenarios where causal information is available and interventions can be performed, such as clinical trials or policy-making. In the single-source domain, Causal Bayesian Optimization (CBO) extends standard BO with the principles of causality, enabling better modeling of variable dependencies. This leads to more accurate optimization, improved decision making, and more efficient use of low-cost information sources. In this article, we propose a principled integration of the MSBO and CBO methodologies in the multi-source domain, leveraging the strengths of both to enhance optimization efficiency and reduce computational complexity in higher-dimensional problems. We present the theoretical foundations of both Causal and Multi-Source Bayesian Optimization, and demonstrate how their synergy informs our Multi-Source Causal Bayesian Optimization (MSCBO) algorithm. We compare the performance of MSCBO against its foundational counterparts for both synthetic and real-world datasets with varying levels of noise, highlighting the robustness and applicability of MSCBO. Based on our findings, we conclude that integrating MSBO with the causality principles of CBO facilitates dimensionality reduction and lowers operational costs, ultimately improving convergence speed, performance, and scalability.

PDF

AAMAS Conference 2021 Conference Paper

Accelerating Recursive Partition-Based Causal Structure Learning

Md. Musfiqur Rahman
Ayman Rasheed
Md. Mosaddek Khan
Mohammad Ali Javidian
Pooyan Jamshidi
Md. Mamun-Or-Rashid

Causal structure discovery from observational data is fundamental to the causal understanding of autonomous systems such as medical decision support systems, advertising campaigns and selfdriving cars. This is essential to solve well-known causal decision making and prediction problems associated with those real-world applications. Recently, recursive causal discovery algorithms have gained particular attention among the research community due to their ability to provide good results by using Conditional Independent (CI) tests in smaller sub-problems. However, each of such algorithms needs a refinement function to remove undesired causal relations of the discovered graphs. Notably, with the increase of the problem size, the computation cost (i. e. , the number of CI-tests) of the refinement function makes an algorithm expensive to deploy in practice. This paper proposes a generic causal structure refinement strategy that can locate the undesired relations with a small number of CI-tests, thus speeding up the algorithm for large and complex problems. We theoretically prove the correctness of our algorithm. We then empirically evaluate its performance against the state-ofthe-art algorithms in terms of solution quality and completion time in synthetic and real datasets.

PDF

JAIR Journal 2020 Journal Article

AMP Chain Graphs: Minimal Separators and Structure Learning Algorithms

Mohammad Ali Javidian
Marco Valtorta
Pooyan Jamshidi

This paper deals with chain graphs (CGs) under the Andersson–Madigan–Perlman (AMP) interpretation. We address the problem of finding a minimal separator in an AMP CG, namely, finding a set Z of nodes that separates a given non-adjacent pair of nodes such that no proper subset of Z separates that pair. We analyze several versions of this problem and offer polynomial time algorithms for each. These include finding a minimal separator from a restricted set of nodes, finding a minimal separator for two given disjoint sets, and testing whether a given separator is minimal. To address the problem of learning the structure of AMP CGs from data, we show that the PC-like algorithm is order dependent, in the sense that the output can depend on the order in which the variables are given. We propose several modifications of the PC-like algorithm that remove part or all of this order-dependence. We also extend the decomposition-based approach for learning Bayesian networks (BNs) to learn AMP CGs, which include BNs as a special case, under the faithfulness assumption. We prove the correctness of our extension using the minimal separator results. Using standard benchmarks and synthetically generated models and data in our experiments demonstrate the competitive performance of our decomposition-based method, called LCD-AMP, in comparison with the (modified versions of) PC-like algorithm. The LCD-AMP algorithm usually outperforms the PC-like algorithm, and our modifications of the PC-like algorithm learn structures that are more similar to the underlying ground truth graphs than the original PC-like algorithm, especially in high-dimensional settings. In particular, we empirically show that the results of both algorithms are more accurate and stabler when the sample size is reasonably large and the underlying graph is sparse

PDF Details DOI

UAI Conference 2020 Conference Paper

Learning LWF Chain Graphs: A Markov Blanket Discovery Approach

Mohammad Ali Javidian
Marco Valtorta
Pooyan Jamshidi

This paper provides a graphical characterization of Markov blankets in chaingraphs (CGs) under the Lauritzen-Wermuth-Frydenberg (LWF) interpretation. The characterization is different from the well-known one for Bayesian networks and generalizes it. We provide a novel scalable and sound algorithmfor Markov blanket discovery in LWF CGs and prove that the Grow-Shrink algorithm, the IAMB algorithm, and its variants are still correct for Markov blanket discovery in LWF CGs under the same assumptions as for Bayesian networks. We provide a sound and scalable constraint-based framework for learning the structure of LWF CGs from faithful causally sufficient data and prove its correctness when the Markov blanket discovery algorithms in this paper are used. Our proposed algorithms compare positively/competitively against the state-of-the-art LCD (Learn Chain graphs via Decomposition) algorithm, depending on the algorithm that is used for Markov blanket discovery. Our proposed algorithms make a broad range of inference/learning problems computationallytractable and more reliable because they exploit locality.

Details

AAMAS Conference 2019 Conference Paper

Avoiding Social Disappointment in Elections

Mohammad Ali Javidian
Pooyan Jamshidi
Rasoul Ramezanian

Mechanism design is concerned with settings where a policy maker (or social planner) faces the problem of aggregating the announced preferences of multiple agents into a collective (or social), systemwide decision. One of the most important ways for aggregating preference that has been used in multi-agent systems is election. In an election, the aim is to select the candidate who reflects the common will of society. Despite the importance of this subject, in real-world situations, under special circumstances, the result of the election does not respect the purpose of those who execute it and the election leads to dissatisfaction of a large amount of people and in some cases causes polarization in societies. To analyze these situations, we introduce a new notion called social disappointment and we show which voting rules can prevent it in elections. In addition, we propose new protocols to prevent social disappointment in elections. A version of the impossibility theorem is proved regarding social disappointment in elections, showing that there is no voting rule for four or more candidates that simultaneously satisfies avoiding social disappointment and Condorcet winner criteria. We empirically compared our protocols with seven well-known voting protocols and we observed that our protocols are capable of preventing social disappointment and are more robust against manipulations.

PDF