Ricardo Silva

AGM-TE: Approximate Generative Model Estimator of Transfer Entropy for Causal Discovery CLeaR 2025 BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments AISTATS 2025 Bounding causal effects with leaky instruments UAI 2024 Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models NeurIPS 2024 Pragmatic Fairness: Developing Policies with Outcome Disparity Control CLeaR 2024 Structured Learning of Compositional Sequential Interventions NeurIPS 2024 Intervention Generalization: A View from Factor Graph Models NeurIPS 2023 Stochastic Causal Programming for Bounding Treatment Effects CLeaR 2023 Causal discovery under a confounder blanket UAI 2022 Causal inference with treatment measurement error: a nonparametric instrumental variable approach UAI 2022 When Do Flat Minima Optimizers Work? NeurIPS 2022 Causal Effect Inference for Structured Treatments NeurIPS 2021 Operationalizing Complex Causes: A Pragmatic View of Mediation ICML 2021 Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction ICML 2021 A Class of Algorithms for General Instrumental Variable Models NeurIPS 2020 Differentiable Causal Backdoor Discovery AISTATS 2020 Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders UAI 2020 Neural Likelihoods via Cumulative Distribution Functions UAI 2020 Making Decisions that Reduce Discriminatory Impacts ICML 2019 The Sensitivity of Counterfactual Fairness to Unmeasured Confounding UAI 2019 Bayesian Semi-supervised Learning with Graph Gaussian Processes NeurIPS 2018 Counterfactual Fairness NeurIPS 2017 Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions JMLR 2017 Tomography of the London Underground: a Scalable Model for Origin-Destination Data NeurIPS 2017 When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness NeurIPS 2017 Causal Inference through a Witness Protection Program JMLR 2016 Observational-Interventional Priors for Dose-Response Learning NeurIPS 2016 Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages NeurIPS 2016 Causal Inference through a Witness Protection Program NeurIPS 2014 Flexible sampling of discrete data correlations without the marginal distributions NeurIPS 2013 Discussion of “Learning Equivalence Classes of Acyclic Models with Latent and Selection Variables from Multiple Datasets with Overlapping Variables” AISTATS 2011 Mixed Cumulative Distribution Networks AISTATS 2011 Thinning Measurement Models and Questionnaire Design NeurIPS 2011 The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models JMLR 2009 Hidden Common Cause Relations in Relational Learning NeurIPS 2007 Learning the Structure of Linear Latent Variable Models JMLR 2006 Factorial Mixture of Gaussians and the Marginal Independence Model AISTATS 2005 MCMC Methods for Bayesian Mixtures of Copulas AISTATS 2005 Analogical Reasoning with Relational Bayesian Sets AISTATS 1999