Search Results for author: Ricardo Silva

Found 42 papers, 16 papers with code

Structured Learning of Compositional Sequential Interventions

no code implementations9 Jun 2024 Jialin Yu, Andreas Koukorinis, Nicolò Colombo, Yuchen Zhu, Ricardo Silva

We consider sequential treatment regimes where each unit is exposed to combinations of interventions over time.

Matrix Completion

Bounding Causal Effects with Leaky Instruments

1 code implementation5 Apr 2024 David S. Watson, Jordan Penn, Lee M. Gunderson, Gecia Bravo-Hermsdorff, Afsaneh Mastouri, Ricardo Silva

Instrumental variables (IVs) are a popular and powerful tool for estimating causal effects in the presence of unobserved confounding.

Counterfactual Fairness Is Not Demographic Parity, and Other Observations

no code implementations5 Feb 2024 Ricardo Silva

Blanket statements of equivalence between causal concepts and purely probabilistic concepts should be approached with care.

counterfactual Fairness

Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases

2 code implementations9 Mar 2023 Aengus Lynch, Gbètondji J-S Dovonon, Jean Kaddour, Ricardo Silva

The problem of spurious correlations (SCs) arises when a classifier relies on non-predictive features that happen to be correlated with the labels in the training data.

Image Captioning Image Classification

Causal Machine Learning: A Survey and Open Problems

no code implementations30 Jun 2022 Jean Kaddour, Aengus Lynch, Qi Liu, Matt J. Kusner, Ricardo Silva

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM).

BIG-bench Machine Learning Fairness +1

Causal discovery under a confounder blanket

1 code implementation11 May 2022 David S. Watson, Ricardo Silva

Under a structural assumption called the $\textit{confounder blanket principle}$, which we argue is essential for tractable causal discovery in high dimensions, our method accommodates graphs of low or high sparsity while maintaining polynomial time complexity.

Causal Discovery

The Causal Marginal Polytope for Bounding Treatment Effects

no code implementations28 Feb 2022 Jakob Zeitler, Ricardo Silva

Due to unmeasured confounding, it is often not possible to identify causal effects from a postulated model.

Stochastic Causal Programming for Bounding Treatment Effects

1 code implementation22 Feb 2022 Kirtan Padh, Jakob Zeitler, David Watson, Matt Kusner, Ricardo Silva, Niki Kilbertus

Causal effect estimation is important for many tasks in the natural and social sciences.

When Do Flat Minima Optimizers Work?

1 code implementation1 Feb 2022 Jean Kaddour, Linqing Liu, Ricardo Silva, Matt J. Kusner

Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers.

Benchmarking Graph Learning +9

Operationalizing Complex Causes: A Pragmatic View of Mediation

1 code implementation9 Jun 2021 Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva

We examine the problem of causal response estimation for complex objects (e. g., text, images, genomics).

Causal Effect Inference for Structured Treatments

2 code implementations NeurIPS 2021 Jean Kaddour, Yuchen Zhu, Qi Liu, Matt J. Kusner, Ricardo Silva

We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e. g., graphs, images, texts).

Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction

2 code implementations10 May 2021 Afsaneh Mastouri, Yuchen Zhu, Limor Gultchin, Anna Korba, Ricardo Silva, Matt J. Kusner, Arthur Gretton, Krikamol Muandet

In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting.

Vocal Bursts Valence Prediction

A Class of Algorithms for General Instrumental Variable Models

1 code implementation NeurIPS 2020 Niki Kilbertus, Matt J. Kusner, Ricardo Silva

Causal treatment effect estimation is a key problem that arises in a variety of real-world settings, from personalized medicine to governmental policy making.

Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders

no code implementations23 May 2020 Sorawit Saengkyongam, Ricardo Silva

We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders.

Differentiable Causal Backdoor Discovery

1 code implementation3 Mar 2020 Limor Gultchin, Matt J. Kusner, Varun Kanade, Ricardo Silva

Discovering the causal effect of a decision is critical to nearly all forms of decision-making.

Decision Making

Adversarial recovery of agent rewards from latent spaces of the limit order book

no code implementations9 Dec 2019 Jacobo Roa-Vicens, Yuanbo Wang, Virgile Mison, Yarin Gal, Ricardo Silva

In this paper, we explore whether adversarial inverse RL algorithms can be adapted and trained within such latent space simulations from real market data, while maintaining their ability to recover agent rewards robust to variations in the underlying dynamics, and transfer them to new regimes of the original environment.

Counterfactual Distribution Regression for Structured Inference

no code implementations20 Aug 2019 Nicolo Colombo, Ricardo Silva, Soong M Kang, Arthur Gretton

The inference problem is how information concerning perturbations, with particular covariates such as location and time, can be generalized to predict the effect of novel perturbations.

counterfactual regression

The Sensitivity of Counterfactual Fairness to Unmeasured Confounding

1 code implementation1 Jul 2019 Niki Kilbertus, Philip J. Ball, Matt J. Kusner, Adrian Weller, Ricardo Silva

We demonstrate our new sensitivity analysis tools in real-world fairness scenarios to assess the bias arising from confounding.

counterfactual Fairness

Towards Inverse Reinforcement Learning for Limit Order Book Dynamics

no code implementations11 Jun 2019 Jacobo Roa-Vicens, Cyrine Chtourou, Angelos Filos, Francisco Rullan, Yarin Gal, Ricardo Silva

Given the expert agent's demonstrations, we attempt to discover their strategy by modelling their latent reward function using linear and Gaussian process (GP) regressors from previous literature, and our own approach through Bayesian neural networks (BNN).

reinforcement-learning Reinforcement Learning (RL)

Neural Likelihoods via Cumulative Distribution Functions

2 code implementations2 Nov 2018 Pawel Chilinski, Ricardo Silva

We leverage neural networks as universal approximators of monotonic functions to build a parameterization of conditional cumulative distribution functions (CDFs).

Density Estimation

Bayesian Semi-supervised Learning with Graph Gaussian Processes

2 code implementations NeurIPS 2018 Yin Cheng Ng, Nicolo Colombo, Ricardo Silva

We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs.

Active Learning Gaussian Processes +1

Causal Interventions for Fairness

no code implementations6 Jun 2018 Matt J. Kusner, Chris Russell, Joshua R. Loftus, Ricardo Silva

Most approaches in algorithmic fairness constrain machine learning methods so the resulting predictions satisfy one of several intuitive notions of fairness.


Causal Reasoning for Algorithmic Fairness

no code implementations15 May 2018 Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva

In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making.

Decision Making Fairness

Alpha-Beta Divergence For Variational Inference

no code implementations2 May 2018 Jean-Baptiste Regli, Ricardo Silva

This paper introduces a variational approximation framework using direct optimization of what is known as the {\it scale invariant Alpha-Beta divergence} (sAB divergence).

Variational Inference

Two-way sparsity for time-varying networks, with applications in genomics

1 code implementation22 Feb 2018 Thomas E. Bartlett, Ioannis Kosmidis, Ricardo Silva

Separation of these two types of sparsity is achieved with the introduction of a novel prior structure, which draws on ideas from the Bayesian lasso and from copula modelling.


When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness

no code implementations NeurIPS 2017 Chris Russell, Matt J. Kusner, Joshua Loftus, Ricardo Silva

In this paper, we show how it is possible to make predictions that are approximately fair with respect to multiple possible causal models at once, thus mitigating the problem of exact causal specification.

counterfactual Counterfactual Inference +1

Tomography of the London Underground: a Scalable Model for Origin-Destination Data

no code implementations NeurIPS 2017 Nicolò Colombo, Ricardo Silva, Soong Moon Kang

The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations.

Variational Inference

A Dynamic Edge Exchangeable Model for Sparse Temporal Networks

no code implementations11 Oct 2017 Yin Cheng Ng, Ricardo Silva

We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks, in contrast to existing models which are dense.

Link Prediction Variational Inference

Counterfactual Fairness

3 code implementations NeurIPS 2017 Matt J. Kusner, Joshua R. Loftus, Chris Russell, Ricardo Silva

Machine learning can impact people with legal or ethical consequences when it is used to automate decisions in areas such as insurance, lending, hiring, and predictive policing.

BIG-bench Machine Learning Causal Inference +2

Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages

no code implementations NeurIPS 2016 Yin Cheng Ng, Pawel Chilinski, Ricardo Silva

Factorial Hidden Markov Models (FHMMs) are powerful models for sequential data but they do not scale well with long sequences.

Variational Inference

Observational-Interventional Priors for Dose-Response Learning

no code implementations NeurIPS 2016 Ricardo Silva

In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes.

Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms

no code implementations9 Nov 2015 Ricardo Silva, Shohei Shimizu

Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions.

Bayesian Inference in Cumulative Distribution Fields

no code implementations9 Nov 2015 Ricardo Silva

Given that products of cumulative distribution functions (CDFs) are also CDFs, an adjustment to this multiplication will result in a copula model, as discussed by Liebscher (J Mult Analysis, 2008).

Bayesian Inference

Gaussian Process Structural Equation Models with Latent Variables

no code implementations9 Aug 2014 Ricardo Silva, Robert B. Gramacy

In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure.

Causal Inference through a Witness Protection Program

no code implementations NeurIPS 2014 Ricardo Silva, Robin Evans

The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of "path cancellations" that imply conditional independencies but do not rule out the existence of confounding causal paths.

Bayesian Inference Causal Inference

Flexible sampling of discrete data correlations without the marginal distributions

no code implementations NeurIPS 2013 Alfredo Kalaitzis, Ricardo Silva

Learning the joint dependence of discrete variables is a fundamental problem in machine learning, with many applications including prediction, clustering and dimensionality reduction.

Clustering Dimensionality Reduction

Thinning Measurement Models and Questionnaire Design

no code implementations NeurIPS 2011 Ricardo Silva

In particular, an important source of data in fields such as marketing, social sciences and medicine is questionnaires: answers in such questionnaires are noisy measures of target unobserved features.


Ranking relations using analogies in biological and information networks

no code implementations28 Dec 2009 Ricardo Silva, Katherine Heller, Zoubin Ghahramani, Edoardo M. Airoldi

Our work addresses the following question: is the relation between objects A and B analogous to those relations found in $\mathbf{S}$?

Information Retrieval Relational Reasoning +1

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