SCDMs and SCDPs are composable causal decision models that are strictly more expressive than POMDPs by allowing endogenous memory formation and variable discounting without rational belief assumptions.
2009.Causality
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Counter-Dyna reduces RL training data for HVAC control to five weeks by using counterfactual surrogate models that ignore uncontrollable variables like weather and prices.
CausalSynth combines structural causal models with LLMs and iterative verification to produce synthetic data that respects given causal structures while remaining linguistically natural.
Inductive subgraphs serve as shortcuts in heterophilic graphs, and CD-GNN disentangles spurious from causal subgraphs by blocking non-causal paths to improve robustness and accuracy.
citing papers explorer
-
The Design and Composition of Structural Causal Decision Processes
SCDMs and SCDPs are composable causal decision models that are strictly more expressive than POMDPs by allowing endogenous memory formation and variable discounting without rational belief assumptions.
-
Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models
Counter-Dyna reduces RL training data for HVAC control to five weeks by using counterfactual surrogate models that ignore uncontrollable variables like weather and prices.
-
CasualSynth: Generating Structurally Sound Synthetic Data
CausalSynth combines structural causal models with LLMs and iterative verification to produce synthetic data that respects given causal structures while remaining linguistically natural.
-
Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning
Inductive subgraphs serve as shortcuts in heterophilic graphs, and CD-GNN disentangles spurious from causal subgraphs by blocking non-causal paths to improve robustness and accuracy.