Counterfactual Inference
49 papers with code • 0 benchmarks • 2 datasets
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Latest papers
Disentangling ID and Modality Effects for Session-based Recommendation
At the item level, we introduce a co-occurrence representation schema to explicitly incorporate cooccurrence patterns into ID representations.
Counterfactual contrastive learning: robust representations via causal image synthesis
biomedia-mira/counterfactual-contrastive
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Contrastive pretraining is well-known to improve downstream task performance and model generalisation, especially in limited label settings.
Doubly Abductive Counterfactual Inference for Text-based Image Editing
xuesong39/dac
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Through the lens of the formulation, we find that the crux of TBIE is that existing techniques hardly achieve a good trade-off between editability and fidelity, mainly due to the overfitting of the single-image fine-tuning.
Debiasing Recommendation with Personal Popularity
stevenn9981/ppac
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Many methods have been proposed to reduce GP bias but they fail to notice the fundamental problem of GP, i. e., it considers popularity from a \textit{global} perspective of \textit{all users} and uses a single set of popular items, and thus cannot capture the interests of individual users.
Offline Imitation Learning with Variational Counterfactual Reasoning
zexusun/oilca-neurips23
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NeurIPS 2023
We theoretically analyze the influence of the generated expert data and the improvement of generalization.
Simulating counterfactuals
Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world.
Path-Specific Counterfactual Fairness for Recommender Systems
But since sensitive features may also affect user interests in a fair manner (e. g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities.
Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model
valentyn1997/csm-apid
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NeurIPS 2023
We further show that existing point counterfactual identification methods are special cases of our Curvature Sensitivity Model when the bound of the curvature is set to zero.
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
To tackle this problem, we propose a deep learning framework based on `\textit{soft weight sharing}' to train ITE learners, enabling \textit{dynamic end-to-end} information sharing among treatment groups.
Achieving Counterfactual Fairness with Imperfect Structural Causal Model
tridungduong16/counterfactual_fairness_game_theoretic
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Counterfactual fairness alleviates the discrimination between the model prediction toward an individual in the actual world (observational data) and that in counterfactual world (i. e., what if the individual belongs to other sensitive groups).
