Counterfactual Inference
49 papers with code • 0 benchmarks • 2 datasets
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Latest papers
Measuring axiomatic soundness of counterfactual image models
We present a general framework for evaluating image counterfactuals.
Interventional and Counterfactual Inference with Diffusion Models
patrickrchao/diffusionbasedcausalmodels
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We consider the problem of answering observational, interventional, and counterfactual queries in a causally sufficient setting where only observational data and the causal graph are available.
A Counterfactual Collaborative Session-based Recommender System
Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem.
Causal Inference for Knowledge Graph based Recommendation
weiyinwei/kgcr
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Knowledge Graph (KG), as a side-information, tends to be utilized to supplement the collaborative filtering (CF) based recommendation model.
Learning to Bound Counterfactual Inference from Observational, Biased and Randomised Data
We address the problem of integrating data from multiple, possibly biased, observational and interventional studies, to eventually compute counterfactuals in structural causal models.
Counterfactual Learning with Multioutput Deep Kernels
In this paper, we address the challenge of performing counterfactual inference with observational data via Bayesian nonparametric regression adjustment, with a focus on high-dimensional settings featuring multiple actions and multiple correlated outcomes.
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues
The dialogue data admits a wide variety of responses for a given dialogue history, especially responses with different semantics.
Counterfactual Supervision-based Information Bottleneck for Out-of-Distribution Generalization
szubing/csib
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First, we show that the key assumption of support overlap of invariant features used in IB-IRM is strong for the guarantee of OOD generalization and it is still possible to achieve the optimal solution without this assumption.
Counterfactual Reasoning for Out-of-distribution Multimodal Sentiment Analysis
Inspired by this, we devise a model-agnostic counterfactual framework for multimodal sentiment analysis, which captures the direct effect of textual modality via an extra text model and estimates the indirect one by a multimodal model.
User-controllable Recommendation Against Filter Bubbles
wenjiewwj/ucrs
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both accuracy and diversity.
