# Automated Discovery of Pairwise Interactions from Unstructured Data arXiv: https://arxiv.org/abs/2409.07594 ยท HTML: https://mlsanity.com/paper/2409.07594/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 80 references that are in the map. - First posted on arXiv 11 Sep 2024. ## Builds on (most cited first, 30 of 31) - [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018) - [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018) - [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018) - [Kernel Mean Embedding of Distributions: A Review and Beyond](https://mlsanity.com/paper/1605.09522/) (2016) - [Black Box Variational Inference](https://mlsanity.com/paper/1401.0118/) (2013) - [Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning](https://mlsanity.com/paper/1805.08651/) (2018) - [Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA](https://mlsanity.com/paper/1605.06336/) (2016) - [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019) - [An Information-Theoretic Analysis of Thompson Sampling](https://mlsanity.com/paper/1403.5341/) (2014) - [Interventional Causal Representation Learning](https://mlsanity.com/paper/2209.11924/) (2022) - [Understanding the Limitations of Variational Mutual Information Estimators](https://mlsanity.com/paper/1910.06222/) (2019) - [Modern Bayesian Experimental Design](https://mlsanity.com/paper/2302.14545/) (2023) - [On Linear Identifiability of Learned Representations](https://mlsanity.com/paper/2007.00810/) (2020) - [Learning Linear Causal Representations from Interventions under General Nonlinear Mixing](https://mlsanity.com/paper/2306.02235/) (2023) - [Linear Causal Disentanglement via Interventions](https://mlsanity.com/paper/2211.16467/) (2022) - [Identifiability Guarantees for Causal Disentanglement from Soft Interventions](https://mlsanity.com/paper/2307.06250/) (2023) - [Concept Algebra for (Score-Based) Text-Controlled Generative Models](https://mlsanity.com/paper/2302.03693/) (2023) - [RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods](https://mlsanity.com/paper/2301.05768/) (2023) - [Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder](https://mlsanity.com/paper/2311.02794/) (2023) - [Automated Variational Inference in Probabilistic Programming](https://mlsanity.com/paper/1301.1299/) (2013) - [Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling](https://mlsanity.com/paper/2211.03553/) (2022) - [Learning nonparametric latent causal graphs with unknown interventions](https://mlsanity.com/paper/2306.02899/) (2023) - [GeneDisco: A Benchmark for Experimental Design in Drug Discovery](https://mlsanity.com/paper/2110.11875/) (2021) - [DiscoBAX: Discovery of Optimal Intervention Sets in Genomic Experiment Design](https://mlsanity.com/paper/2312.04064/) (2023) - [Season combinatorial intervention predictions with Salt & Peper](https://mlsanity.com/paper/2404.16907/) (2024) - [Masked Autoencoders are Scalable Learners of Cellular Morphology](https://mlsanity.com/paper/2309.16064/) (2023) - [Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space](https://mlsanity.com/paper/2109.14688/) (2021) - [Neural Design for Genetic Perturbation Experiments](https://mlsanity.com/paper/2207.12805/) (2022) ## Built on by (most cited first, 8 of 8) - [Towards Interpretable Deep Generative Models via Causal Representation Learning](https://mlsanity.com/paper/2504.11609/) (2025) - [Virtual Cells: Predict, Explain, Discover](https://mlsanity.com/paper/2505.14613/) (2025) - [From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?](https://mlsanity.com/paper/2512.15134/) (2025) - [The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations](https://mlsanity.com/paper/2505.17708/) (2025) - [Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate](https://mlsanity.com/paper/2606.23920/) (2026) - [Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics](https://mlsanity.com/paper/2502.01012/) (2025) - [Extrapolation Guarantees for Perturbation Modeling Under the Additive Latent Shift Assumption](https://mlsanity.com/paper/2504.18522/) (2025) - [Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors](https://mlsanity.com/paper/2606.08816/) (2026) ## Abstract (arXiv) Pairwise interactions between perturbations to a system can provide evidence for the causal dependencies of the underlying underlying mechanisms of a system. When observations are low dimensional, hand crafted measurements, detecting interactions amounts to simple statistical tests, but it is not obvious how to detect interactions between perturbations affecting latent variables. We derive two interaction tests that are based on pairwise interventions, and show how these tests can be integrated into an active learning pipeline to efficiently discover pairwise interactions between perturbations. We illustrate the value of these tests in the context of biology, where pairwise perturbation experiments are frequently used to reveal interactions that are not observable from any single perturbation. Our tests can be run on unstructured data, such as the pixels in an image, which enables a more general notion of interaction than typical cell viability experiments, and can be run on cheaper experimental assays. We validate on several synthetic and real biological experiments that our tests are able to identify interacting pairs effectively. We evaluate our approach on a real biological experiment where we knocked out 50 pairs of genes and measured the effect with microscopy images. We show that we are able to recover significantly more known biological interactions than random search and standard active learning baselines.