# CausalML Lab

Causal reasoning is essential for artificial intelligence. In CausalML lab, we develop new theoretical results that give us insights about some of the causal discovery and inference problems, and develop novel algorithms based on these insights. Our research can be broadly categorized into multiple pillars *i) information theoretic causal inference and discovery, ii) experimental design for causal discovery, iii) causal discovery from interventional data, iv) applications of causality in machine learning.*

# Current Members

*PostDoc Researchers*

- Lai Wei

*PhD Students*

# Projects

Our groupâ€™s research is focused on developing fundamental algorithms for causal discovery and inference from data. Some threads we focus on are as follows:

*Information Theoretic Causal Inference and Discovery from Observational Data*

We would like to extend the limits of the existing causal discovery algorithms from observational data. We establish connections with information theory and develop efficient algorithms for causal structure discovery using these connections.

### Related publications:

- Z. Jiang, L. Wei, M. Kocaoglu, â€śApproximate Causal Effect Identification under Weak Confounding,â€ť in Proc. of
**ICMLâ€™23**, Honolulu, HI, USA, July 2023. - S. Compton, D. Katz, B. Qi, K. Greenewald, M. Kocaoglu, â€śMinimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier,â€ť in Proc. of
**AISTATSâ€™23**, Valencia, Spain, April 2023. - S. Compton, K. Greenewald, D. Katz, M. Kocaoglu, â€śEntropic Causal Inference: Graph Identifiabilityâ€ť, in Proc. of
**ICMLâ€™22**, July 2022. - S. Compton, M. Kocaoglu, Kristjan Greenewald, Dmitriy Katz, â€śEntropic Causal Inference: Identifiability and Finite Sample Results,â€ť in Proc. of
**NeurIPSâ€™20**, Online, Dec. 2020. - M. Kocaoglu, S. Shakkottai, A. G. Dimakis, C. Caramanis, S. Vishwanath, â€śApplications of Common Entropy for Causal Inference,â€ť in Proc. of
**NeurIPSâ€™20**, Online, Dec. 2020. - M. Kocaoglu, A. G. Dimakis, S. Vishwanath, B. Hassibi, â€śEntropic Causality and Greedy Minimum Entropy Coupling,â€ť in Proc. of
**ISITâ€™17**, 2017. - M. Kocaoglu, A. G. Dimakis, S. Vishwanath, B. Hassibi, â€śEntropic Causal Inference,â€ť in Proc. of
**AAAIâ€™17**, San Francisco, USA, Feb. 2017.

*Experimental Design for Causal Discovery*

In many settings, further experimentation is possible in order to aid with causal structure discovery. We seek out methods to be as efficient as possible with these experimental designs. Efficiency can mean different things in different contexts, for example, using the smallest possible number of experiments or minimizing an arbitrary modular cost function.

### Related publications

- C. Squires, S. Magliacane, K. Greenewald, D. Katz, M. Kocaoglu, K. Shanmugam, â€śActive Structure Learning of Causal DAGs via Directed Clique Trees,â€ť in Proc. of
**NeurIPSâ€™20**, Online, Dec. 2020. - K. Greenewald, D. Katz, K. Shanmugam, S. Magliacane, M. Kocaoglu, E. B. Adsera, G. Bresler, â€śSample Efficient Active Learning of Causal Trees,â€ť in Proc. of
**NeurIPSâ€™19**, Vancouver, Canada, Dec. 2019. - E. Lindgren, M. Kocaoglu, A. G. Dimakis, S. Vishwanath, â€śExperimental Design for Cost-Aware Learning of Causal Graphsâ€ť in Proc. of
**NeurIPSâ€™18**, Montreal, Canada, Dec. 2018. - E. Lindgren, M. Kocaoglu, A. G. Dimakis, S. Vishwanath, â€śSubmodularity and Minimum Cost Intervention Design for Learning Causal Graphs,â€ť in
**DISCMLâ€™17 Workshop in NIPSâ€™17**, Dec. 2017. - M. Kocaoglu, K. Shanmugam, E. Bareinboim, â€śExperimental Design for Learning Causal Graphs with Latent Variables,â€ť in Proc. of
**NeurIPSâ€™17**, 2017. - M. Kocaoglu, A. G. Dimakis, S. Vishwanath, â€śCost-Optimal Learning of Causal Graphs,â€ť in Proc. of
**ICMLâ€™17**, 2017. - M. Kocaoglu, A. G. Dimakis, S. Vishwanath, â€śLearning Causal Graphs with Constraints,â€ť in
**NeurIPSâ€™16 Workshop**: What If? Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems, Barcelona, Spain, Dec. 2016. - K. Shanmugam, M. Kocaoglu, A. G. Dimakis, S. Vishwanath, â€śLearning Causal Graphs with Small Interventions,â€ť in Proc. of
**NeurIPSâ€™15**, Montreal, Canada, Dec. 2015.

*Causal Discovery from Interventional Data*

Causal discovery from interventional data is the golden standard where we can get away with the least amount of assumptions. However one challenge today is that interventions are expensive and we need to make best use of the available interventional data. Especially for large-scale systems, learning the causal structure exhaustively requires too many experiments. We focus on distilling as much information as possible from a given collection of interventional datasets.

### Related publications

- A. Jaber, M. Kocaoglu, K. Shanmugam, E. Bareinboim, â€śCausal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning,â€ť in Proc. of
**NeurIPSâ€™20**, Online, Dec. 2020. - M. Kocaoglu*, A. Jaber*, K. Shanmugam*, E. Bareinboim, â€śCharacterization and Learning of Causal Graphs with Latent Variables from Soft Interventions,â€ť in Proc. of
**NeurIPSâ€™19**, Vancouver, Canada, Dec. 2019.

*Applications of Causality in Machine Learning*

We explore ways in which causal inference and discovery can help machine learning.

### Related publications

- K. Lee, M. M. Rahman, M. Kocaoglu, â€śFinding Invariant Predictors Efficiently via Causal Structure,â€ť in Proc. of UAIâ€™23, Pittsburgh, PA, USA, Aug. 2023.
- M. A. Ikram, S. Chakraborty, S. Mitra, S. Saini, S. Bagchi, M. Kocaoglu, â€śRoot Cause Analysis of Failures in Microservices through Causal Discovery,â€ť in Proc. of
**NeurIPSâ€™22**, Dec. 2022. - K. Ahuja, P. Sattigeri, K. Shanmugam, D. Wei, K. N. Ramamurthy, M. Kocaoglu, â€śConditionally Independent Data Generationâ€ť, in Proc. of
**UAIâ€™21**, 2021. - M. Kocaoglu*, C. Snyder*, A. G. Dimakis, S. Vishwanath, â€śCausalGAN: Learning Causal Implicit Generative Models with Adversarial Training,â€ť in Proc. of
**ICLRâ€™18**, Vancouver, Canada, May 2018. - R. Sen, K. Shanmugam, M. Kocaoglu, A. G. Dimakis, S. Shakkottai, â€śContextual Bandits with Latent Confounders: An NMF Approach,â€ť in Proc. of
**AISTATSâ€™17**, 2017.

# Past Members

## Visiting Researcher

- Suyeong Park,
*July - August 2022*đź“„

# Acknowledgement

Our lab is currently supported by funding from the National Science Foundation (NSF), and Adobe Research.