Counterfactual Inference
48 papers with code • 0 benchmarks • 2 datasets
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Latest papers with no code
Towards Multimodal Sentiment Analysis Debiasing via Bias Purification
In the inference phase, given a factual multimodal input, MCIS imagines two counterfactual scenarios to purify and mitigate these biases.
Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
Adaptive experimental design (AED) methods are increasingly being used in industry as a tool to boost testing throughput or reduce experimentation cost relative to traditional A/B/N testing methods.
Counterfactual Influence in Markov Decision Processes
Our work addresses a fundamental problem in the context of counterfactual inference for Markov Decision Processes (MDPs).
Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Dataset
We exploit the Morpho-MNIST causal dataset as a case study for exploring our proposed methods for generating counterfacutl explantions.
A General Neural Causal Model for Interactive Recommendation
Mitigation of the survivor bias is achieved though counterfactual consistency.
Answering Causal Queries at Layer 3 with DiscoSCMs-Embracing Heterogeneity
In the realm of causal inference, Potential Outcomes (PO) and Structural Causal Models (SCM) are recognized as the principal frameworks. However, when it comes to Layer 3 valuations -- counterfactual queries deeply entwined with individual-level semantics -- both frameworks encounter limitations due to the degenerative issues brought forth by the consistency rule.
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II
In this paper, we develop a data-driven decision model for determining a cost-effective allocation of preventive treatments to patients at risk.
VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference
By disentangling observed and unobserved confounders, VLUCI constructs a doubly variational inference model to approximate the distribution of unobserved confounders, which are used for inferring more accurate counterfactual outcomes.
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
Extensive experiments on synthetic datasets show that the DRL model performs superiorly in learning de-confounding representations and outperforms state-of-the-art counterfactual inference models for continuous treatment variables.
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition.