因果充分性和必要性和其在不变学习中的应用.pdf

1、探索充分必要因果性probability of sufficient and necessary causes杨梦月伦敦大学学院mengyue.yang.20ucl.ac.uk1 The OOD generalization taskInvariant Learning2TrainTest Invariant causal assumption across source and test distribution P(Y|C=c)=P(Y|C=c)Extract causal feature for OOD generalization.Infer causal feature from o

2、bservation data Predict label y from causal featureInvariant Learning3 Is causal representation enough in invariant learning?What kind of causal information is essential?Is that a cat?No!Causal representation4 Defining the sufficient and necessary causes.Chapter 9 in book:Causality Considering the c

3、ounterfactual probability on binary variables X and YCausal representation5 Understanding PNSCausal representation6 Understanding PNSCausal representation7 How to identify PNS from observational data Exogeneity:X is the cause of Y Monotonicity:Changes on X lead to monotonically changes on YCausal re

4、presentation8 How to identify PNS from observational data Exogeneity:X is the cause of Y Monotonicity:Changes on X lead to monotonically changes on YCausal representation9 Defining the PNS risk on test domain Defining Monotonicity measurement.The PNS risk modelingYang,Mengyue,et al.Invariant Learnin

5、g via Probability of Sufficient and Necessary Causes.arXiv preprint(NeurIPS2023 Spotlight).PNS RiskSatisfaction ofMonotonicitySatisfaction ofExogeneity10 Connecting the Monotonicity measurement with PNS riskSatisfaction of Monotonicity11 Exogeneity under different causal assumption 1.C contain all i

6、nformation of Y in X 2.There are no spurious correlation between causal information and domain knowledge 3.C contain not all information of Y in XSatisfaction of ExogeneityYang,Mengyue,et al.Invariant Learning via Probability of Sufficient and Necessary Causes.arXiv preprint(NeurIPS2023 Spotlight).1