Counterfactual Reasoning
61 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
eugenevinitsky/sequential_social_dilemma_games
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ICLR 2019
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions.
Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning
RodrigoToroIcarte/reward_machines
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First, we propose reward machines, a type of finite state machine that supports the specification of reward functions while exposing reward function structure.
Explaining by Removing: A Unified Framework for Model Explanation
We describe a new unified class of methods, removal-based explanations, that are based on the principle of simulating feature removal to quantify each feature's influence.
Counterfactual Phenotyping with Censored Time-to-Events
Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring.
Transformers as Soft Reasoners over Language
However, expressing the knowledge in a formal (logical or probabilistic) representation has been a major obstacle to this research.
Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect
KaihuaTang/Long-Tailed-Recognition.pytorch
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NeurIPS 2020
On one hand, it has a harmful causal effect that misleads the tail prediction biased towards the head.
Counterfactual Explainable Recommendation
Technically, for each item recommended to each user, CountER formulates a joint optimization problem to generate minimal changes on the item aspects so as to create a counterfactual item, such that the recommendation decision on the counterfactual item is reversed.
COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics
Many applications of text generation require incorporating different constraints to control the semantics or style of generated text.
Counterfactual Story Reasoning and Generation
qkaren/Counterfactual-StoryRW
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IJCNLP 2019
Counterfactual reasoning requires predicting how alternative events, contrary to what actually happened, might have resulted in different outcomes.
Counterfactual diagnosis
We show that this approach is closer to the diagnostic reasoning of clinicians and significantly improves the accuracy and safety of the resulting diagnoses.
