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Causal Enhanced Uplift Model
Uplift modeling refers to approaches to quantify net difference in outcome between applying a treatment and not applying it to an individual. It is a...
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River runoff causal discovery with deep reinforcement learning
AbstractCausal discovery from river runoff data aids flood prevention and mitigation strategies, garnering attention in climate and earth science....
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Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions
Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost...
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Causal view mechanism for adversarial domain adaptation
Studies show that the challenge for adversarial domain adaptation is learning domain-invariant representations and alleviating the domain gap....
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Causal Inference with Heterogeneous Confounding Data: A Penalty Approach
Causal inference directly explores the causality among variables, in which average causal effect estimation is a fundamental task. But for...
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On the Logic of Interventionist Counterfactuals Under Indeterministic Causal Laws
We investigate the generalization of causal models to the case of indeterministic causal laws that was suggested in Halpern (2000). We give an...
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Causal Connectivity Transition from Action Observation to Mentalizing Network for Understanding Other’s Action Intention
The previous neuroimaging studies have found that two major cognitive sub-processes, action perception and mental inference, participate in...
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Overcoming Language Priors with Counterfactual Inference for Visual Question Answering
Recent years have seen a lot of efforts in attacking the issue of language priors in the field of Visual Question Answering (VQA). Among the...
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Latent Causal Dynamics Model for Model-Based Reinforcement Learning
Learning an accurate dynamics model is the key task for model-based reinforcement learning (MBRL). Most existing MBRL methods learn the dynamics...
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A Model of Agential Learning Using Active Inference
Agential learning refers to the process of forming beliefs regarding one’s degree of control over actions and outcomes in their environment. We first...
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Improved baselines for causal structure learning on interventional data
Causal structure learning (CSL) refers to the estimation of causal graphs from data. Causal versions of tools such as ROC curves play a prominent...
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Differentiable Causal Discovery Under Heteroscedastic Noise
We consider the problem of estimating directed acyclic graphs from observational data. Many studies on functional causal models assume the...
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Learning Type Inference for Enhanced Dataflow Analysis
Statically analyzing dynamically-typed code is a challenging endeavor, as even seemingly trivial tasks such as determining the targets of procedure...
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Causal Intervention Learning for Multi-person Pose Estimation
Most of learning targets for multi-person pose estimation are based on the likelihood...
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Multi-Criteria Decision Making (MCDM) with Causal Reasoning for AI/ML Applications – A Survey
Multi-criteria decision making (MCDM) refers to making the best possible decision out of different alternatives based on factors which can sometimes...
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Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals. In this setting, a...
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Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch
We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs), a class of Neural Networks designed for...
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Chest X-ray Image Classification: A Causal Perspective
The chest X-ray (CXR) is a widely used and easily accessible medical test for diagnosing common chest diseases. Recently, there have been numerous...
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Causal Discovery with Missing Data in a Multicentric Clinical Study
Causal inference for testing clinical hypotheses from observational data presents many difficulties because the underlying data-generating model and...
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Data Imputation with Adversarial Neural Networks for Causal Discovery from Subsampled Time Series
A relevant and challenging problem is causal discovery from time series data. This helps to understand dynamics events present in real world...