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Found 36 papers, 19 papers with code
Generative Intervention Models for Causal Perturbation Modeling
We consider the problem of predicting perturbation effects via causal models.
Projected Neural Differential Equations for Learning Constrained Dynamics
Neural differential equations offer a powerful approach for learning dynamics from data.
Causal machine learning for predicting treatment outcomes
Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs.
Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series
The robustness of our method across various settings highlights its potential for broad applicability in personalized healthcare solutions.
Learning Representations of Instruments for Partial Identification of Treatment Effects
Our contributions are three-fold: (1) We propose a novel approach for partial identification through a mapping of instruments to a discrete representation space so that we yield valid bounds on the CATE.
Towards Physically Consistent Deep Learning For Climate Model Parameterizations
Climate models play a critical role in understanding and projecting climate change.
Targeted Sequential Indirect Experiment Design
Scientific hypotheses typically concern specific aspects of complex, imperfectly understood or entirely unknown mechanisms, such as the effect of gene expression levels on phenotypes or how microbial communities influence environmental health.
Whole Genome Transformer for Gene Interaction Effects in Microbiome Habitat Specificity
Leveraging the vast genetic diversity within microbiomes offers unparalleled insights into complex phenotypes, yet the task of accurately predicting and understanding such traits from genomic data remains challenging.
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Inferring the causal structure underlying stochastic dynamical systems from observational data holds great promise in domains ranging from science and health to finance.
Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
We hence establish UOT-FM as a principled method for unpaired image translation.
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers
We introduce ODEFormer, the first transformer able to infer multidimensional ordinary differential equation (ODE) systems in symbolic form from the observation of a single solution trajectory.
Predicting Ordinary Differential Equations with Transformers
We develop a transformer-based sequence-to-sequence model that recovers scalar ordinary differential equations (ODEs) in symbolic form from irregularly sampled and noisy observations of a single solution trajectory.
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
Many successful methods to learn dynamical systems from data have recently been introduced.
Sequential Underspecified Instrument Selection for Cause-Effect Estimation
Instrumental variable (IV) methods are used to estimate causal effects in settings with unobserved confounding, where we cannot directly experiment on the treatment variable.
Discovering ordinary differential equations that govern time-series
Natural laws are often described through differential equations yet finding a differential equation that describes the governing law underlying observed data is a challenging and still mostly manual task.
Sparsity in Continuous-Depth Neural Networks
Neural Ordinary Differential Equations (NODEs) have proven successful in learning dynamical systems in terms of accurately recovering the observed trajectories.
Learning Counterfactually Invariant Predictors
Notions of counterfactual invariance (CI) have proven essential for predictors that are fair, robust, and generalizable in the real world.
Modeling Content Creator Incentives on Algorithm-Curated Platforms
To this end, we propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets.
Multi-disciplinary fairness considerations in machine learning for clinical trials
While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice.
Supervised Learning and Model Analysis with Compositional Data
We propose KernelBiome, a kernel-based nonparametric regression and classification framework for compositional data.
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells.
Stochastic Causal Programming for Bounding Treatment Effects
Causal effect estimation is important for many tasks in the natural and social sciences.
On component interactions in two-stage recommender systems
Thanks to their scalability, two-stage recommenders are used by many of today's largest online platforms, including YouTube, LinkedIn, and Pinterest.
Beyond Predictions in Neural ODEs: Identification and Interventions
Spurred by tremendous success in pattern matching and prediction tasks, researchers increasingly resort to machine learning to aid original scientific discovery.
Instrumental Variable Estimation for Compositional Treatments
Many scientific datasets are compositional in nature.
Beyond traditional assumptions in fair machine learning
Revisiting this limitation from a causal viewpoint we develop a more versatile conceptual framework, causal fairness criteria, and first algorithms to achieve them.
Exploration in two-stage recommender systems
In this work, we focus on the complementary issue of exploration.
On Disentangled Representations Learned From Correlated Data
The focus of disentanglement approaches has been on identifying independent factors of variation in data.
A Class of Algorithms for General Instrumental Variable Models
Causal treatment effect estimation is a key problem that arises in a variety of real-world settings, from personalized medicine to governmental policy making.
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
We demonstrate our new sensitivity analysis tools in real-world fairness scenarios to assess the bias arising from confounding.
Convolutional neural networks: a magic bullet for gravitational-wave detection?
In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes.
Fair Decisions Despite Imperfect Predictions
In this paper, we show that in this selective labels setting, learning a predictor directly only from available labeled data is suboptimal in terms of both fairness and utility.
Generalization in anti-causal learning
Anti-causal models are used to drive this search, but a causal model is required for validation.
Blind Justice: Fairness with Encrypted Sensitive Attributes
Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race.
Learning Independent Causal Mechanisms
The approach is unsupervised and based on a set of experts that compete for data generated by the mechanisms, driving specialization.
Avoiding Discrimination through Causal Reasoning
Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning.
