Joint Models of Disagreement and Stance in Online Debate. - Dhanya Sridhar; James R. Foulds; Bert Huang; Lise Getoor; Marilyn A. Walker (ACL (1), 2015)

A probabilistic approach for collective similarity-based drug-drug interaction prediction. - Dhanya Sridhar; Shobeir Fakhraei; Lise Getoor (Bioinform., 2016)

Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks. - Shobeir Fakhraei; Dhanya Sridhar; Jay Pujara; Lise Getoor (CoRR, 2016)

Using Noisy Extractions to Discover Causal Knowledge. - Dhanya Sridhar; Jay Pujara; Lise Getoor (AKBC@NIPS, 2017)

Scalable Probabilistic Causal Structure Discovery. - Dhanya Sridhar; Jay Pujara; Lise Getoor (IJCAI, 2018)

A Structured Approach to Understanding Recovery and Relapse in AA. - Yue Zhang 047; Arti Ramesh; Jennifer Golbeck; Dhanya Sridhar; Lise Getoor (WWW, 2018)

Scalable Structure Learning for Probabilistic Soft Logic. - Varun Embar; Dhanya Sridhar; Golnoosh Farnadi; Lise Getoor (CoRR, 2018)

Learning Structured and Causal Probabilistic Models for Computational Science. - Dhanya Sridhar (2018)

Estimating Causal Effects of Tone in Online Debates. - Dhanya Sridhar; Lise Getoor (IJCAI, 2019)

Equal Opportunity and Affirmative Action via Counterfactual Predictions. - Yixin Wang 002; Dhanya Sridhar; David M. Blei (CoRR, 2019)

Using Text Embeddings for Causal Inference. - Victor Veitch; Dhanya Sridhar; David M. Blei (CoRR, 2019)

Adapting Text Embeddings for Causal Inference. - Victor Veitch; Dhanya Sridhar; David M. Blei (UAI, 2020)

Valid Causal Inference with (Some) Invalid Instruments. - Jason S. Hartford; Victor Veitch; Dhanya Sridhar; Kevin Leyton-Brown (ICML, 2021)

Causal Effects of Linguistic Properties. - Reid Pryzant; Dallas Card; Dan Jurafsky; Victor Veitch; Dhanya Sridhar (NAACL-HLT, 2021)

Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections. - Aaron Schein; Keyon Vafa; Dhanya Sridhar; Victor Veitch; Jeffrey Quinn; James Moffet; David M. Blei; Donald P. Green (WWW, 2021)

Identifiable Variational Autoencoders via Sparse Decoding. - Gemma E. Moran; Dhanya Sridhar; Yixin Wang 002; David M. Blei (CoRR, 2021)

On the Assumptions of Synthetic Control Methods. - Claudia Shi; Dhanya Sridhar; Vishal Misra; David M. Blei (AISTATS, 2022)

Estimating Social Influence from Observational Data. - Dhanya Sridhar; Caterina De Bacco; David M. Blei (CLeaR, 2022)

Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond. - Amir Feder; Katherine A. Keith; Emaad Manzoor; Reid Pryzant; Dhanya Sridhar; Zach Wood-Doughty; Jacob Eisenstein; Justin Grimmer; Roi Reichart; Margaret E. Roberts; Brandon M. Stewart; Victor Veitch; et al. (Trans. Assoc. Comput. Linguistics, 2022)

Heterogeneous Supervised Topic Models. - Dhanya Sridhar; Hal Daumé III; David M. Blei (Trans. Assoc. Comput. Linguistics, 2022)

Identifiable Deep Generative Models via Sparse Decoding. - Gemma E. Moran; Dhanya Sridhar; Yixin Wang 002; David M. Blei (Trans. Mach. Learn. Res., 2022)

Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness. - Yixin Wang 002; Dhanya Sridhar; David M. Blei (Trans. Mach. Learn. Res., 2023)

In-Context Learning Can Re-learn Forbidden Tasks. - Sophie Xhonneux; David Dobre; Jian Tang; Gauthier Gidel; Dhanya Sridhar (CoRR, 2024)

Evaluating Interventional Reasoning Capabilities of Large Language Models. - Tejas Kasetty; Divyat Mahajan; Gintare Karolina Dziugaite; Alexandre Drouin; Dhanya Sridhar (CoRR, 2024)

Demystifying amortized causal discovery with transformers. - Francesco Montagna; Max Cairney-Leeming; Dhanya Sridhar; Francesco Locatello (CoRR, 2024)

Does learning the right latent variables necessarily improve in-context learning? - Sarthak Mittal; Eric Elmoznino; Léo Gagnon; Sangnie Bhardwaj; Dhanya Sridhar; Guillaume Lajoie (CoRR, 2024)

Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations. - Elliot Layne; Jason S. Hartford; Sébastien Lachapelle; Mathieu Blanchette; Dhanya Sridhar (CoRR, 2024)

Causal Representation Learning in Temporal Data via Single-Parent Decoding. - Philippe Brouillard; Sébastien Lachapelle; Julia Kaltenborn; Yaniv Gurwicz; Dhanya Sridhar; Alexandre Drouin; Peer Nowack; Jakob Runge; David Rolnick (CoRR, 2024)

In-context learning and Occam's razor. - Eric Elmoznino; Tom Marty; Tejas Kasetty; Léo Gagnon; Sarthak Mittal; Mahan Fathi; Dhanya Sridhar; Guillaume Lajoie (CoRR, 2024)

General Causal Imputation via Synthetic Interventions. - Marco Jiralerspong; Thomas Jiralerspong; Vedant Shah; Dhanya Sridhar; Gauthier Gidel (CoRR, 2024)

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications. - Philippe Brouillard; Chandler Squires; Jonas Wahl; Konrad P. Kording; Karen Sachs; Alexandre Drouin; Dhanya Sridhar (CoRR, 2024)

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications. - Philippe Brouillard; Chandler Squires; Jonas Wahl; Konrad K; Karen Sachs; Alexandre Drouin; Dhanya Sridhar (CLeaR, 2025)

Sparsity regularization via tree-structured environments for disentangled representations. - Elliot Layne; Jason S. Hartford; Sébastien Lachapelle; Mathieu Blanchette; Dhanya Sridhar (Trans. Mach. Learn. Res., 2025)

Identifiable Steering via Sparse Autoencoding of Multi-Concept Shifts. - Shruti Joshi; Andrea Dittadi; Sébastien Lachapelle; Dhanya Sridhar (CoRR, 2025)

Next-Token Prediction Should be Ambiguity-Sensitive: A Meta-Learning Perspective. - Léo Gagnon; Eric Elmoznino; Sarthak Mittal; Tom Marty; Tejas Kasetty; Dhanya Sridhar; Guillaume Lajoie (CoRR, 2025)