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1. Learning and Sampling of Atomic Interventions from Observations

   Bhattacharyya, Arnab; Gayen, Sutanu; Kandasamy, Saravanan; Maran, Ashwin; Vinodchandran, N. V. (July 2020, Proceedings of the 37th International Conference on Machine Learning)

   We study the problem of efficiently estimating the effect of an intervention on a single variable using observational samples. Our goal is to give algorithms with polynomial time and sample complexity in a non-parametric setting. Tian and Pearl (AAAI ’02) have exactly characterized the class of causal graphs for which causal effects of atomic interventions can be identified from observational data. We make their result quantitative. Suppose 𝒫 is a causal model on a set V of n observable variables with respect to a given causal graph G, and let do(x) be an identifiable intervention on a variable X. We show that assuming that G has bounded in-degree and bounded c-components (k) and that the observational distribution satisfies a strong positivity condition: (i) [Evaluation] There is an algorithm that outputs with probability 2/3 an evaluator for a distribution P^ that satisfies TV(P(V | do(x)), P^(V)) < eps using m=O (n/eps^2) samples from P and O(mn) time. The evaluator can return in O(n) time the probability P^(v) for any assignment v to V. (ii) [Sampling] There is an algorithm that outputs with probability 2/3 a sampler for a distribution P^ that satisfies TV(P(V | do(x)), P^(V)) < eps using m=O (n/eps^2) samples from P and O(mn) time. The sampler returns an iid sample from P^ with probability 1 in O(n) time. We extend our techniques to estimate P(Y | do(x)) for a subset Y of variables of interest. We also show lower bounds for the sample complexity, demonstrating that our sample complexity has optimal dependence on the parameters n and eps, as well as if k=1 on the strong positivity parameter. 

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2. Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models

   https://doi.org/10.3390/e24040512  

   Xie, Feng; He, Yangbo; Geng, Zhi; Chen, Zhengming; Hou, Ru; Zhang, Kun (April 2022, Entropy)

   This paper investigates the problem of selecting instrumental variables relative to a target causal influence X→Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria for the instrument validity are not directly testable given observational data, we further show whether and how such graphical criteria can be checked by exploiting our condition. Finally, we develop a method to select the set of candidate instrumental variables given observational data. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method. 

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3. An Empirical Evaluation of Model Completion for Causal Inference

   Zhao, J; Bareinboim, E; Dechter, R (May 2026, AISTAT)

   Model-completion methods learn a full causal generative model consistent with observational data and a given causal graph, and answer interventional queries via probabilistic inference. We empirically compare two approaches presented recently. One approach learns the model using EM, named EM for Causal Inference (EM4CI). The other approach uses neural networks for completion, yielding two neural causal model approaches, MLE–NCM and GAN–NCM. We evaluate these methods on synthetic discrete benchmarks spanning multiple graph families and scales. Results show that EM4CI seems superior on large graphs in terms of accuracy, while NCM-based methods can be competitive on small models but incur substantially higher computational cost. 

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4. An Empirical Evaluation of Model Completion for Causal Inference

   Zhao, J; Bareinboim, E; Dechter, R (May 2026, AISTAT)

   Model-completion methods learn a full causal generative model consistent with observational data and a given causal graph, and answer interventional queries via probabilistic inference. We empirically compare two approaches presented recently. One approach learns the model using EM, named EM for Causal Inference (EM4CI). The other approach uses neural networks for completion, yielding two neural causal model approaches, MLE–NCM and GAN–NCM. We evaluate these methods on synthetic discrete benchmarks spanning multiple graph families and scales. Results show that EM4CI seems superior on large graphs in terms of accuracy, while NCM-based methods can be competitive on small models but incur substantially higher computational cost. 

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5. Out of Distribution Generalization via Interventional Style Transfer in Single-Cell Microscopy

   Pernice, Wolfgang M.; Doron, Michael; Quach, Alex; Pratapa, Aditya; Kenjeyev, Sultan; De Veaux, Nicholas; Hirano, Michio; Caicedo, Juan C. (July 2023, ieee conference on computer vision and pattern recognition workshops)

   Real-world deployment of computer vision systems, including in the discovery processes of biomedical research, requires causal representations that are invariant to contextual nuisances and generalize to new data. Leveraging the internal replicate structure of two novel single-cell fluorescent microscopy datasets, we propose generally applicable tests to assess the extent to which models learn causal representations across increasingly challenging levels of OODgeneralization. We show that despite seemingly strong performance as assessed by other established metrics, both naive and contemporary baselines designed to ward against confounding, collapse to random on these tests. We introduce a new method, Interventional Style Transfer (IST), that substantially improves OOD generalization by generating interventional training distributions in which spurious correlations between biological causes and nuisances are mitigated. We publish our code and datasets. 

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