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Found 12 papers, 9 papers with code
Generative Intervention Models for Causal Perturbation Modeling
We consider the problem of predicting perturbation effects via causal models.
Standardizing Structural Causal Models
Moreover, contrary to the post-hoc standardization of data generated by standard SCMs, we prove that linear iSCMs are less identifiable from prior knowledge on the weights and do not collapse to deterministic relationships in large systems, which may make iSCMs a useful model in causal inference beyond the benchmarking problem studied here.
Active Bayesian Causal Inference
In this work, we propose Active Bayesian Causal Inference (ABCI), a fully-Bayesian active learning framework for integrated causal discovery and reasoning, which jointly infers a posterior over causal models and queries of interest.
BaCaDI: Bayesian Causal Discovery with Unknown Interventions
Inferring causal structures from experimentation is a central task in many domains.
Amortized Inference for Causal Structure Learning
Rather than searching over structures, we train a variational inference model to directly predict the causal structure from observational or interventional data.
Group Testing under Superspreading Dynamics
Testing is recommended for all close contacts of confirmed COVID-19 patients.
DiBS: Differentiable Bayesian Structure Learning
In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation.
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness.
Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots
Multiple lines of evidence strongly suggest that infection hotspots, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19.
Output-Constrained Bayesian Neural Networks
Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space.
Stochastic Optimal Control of Epidemic Processes in Networks
We approach the development of models and control strategies of susceptible-infected-susceptible (SIS) epidemic processes from the perspective of marked temporal point processes and stochastic optimal control of stochastic differential equations (SDEs) with jumps.
