Prophit: Causal inverse classification for multiple continuously valued treatment policies
Inverse classification uses an induced classifier as a queryable oracle to guide test instances towards a preferred posterior class label. The result produced from the process is a set of instance-specific feature perturbations, or recommendations, that optimally improve the probability of the class label. In this work, we adopt a causal approach to inverse classification, eliciting treatment policies (i.e., feature perturbations) for models induced with causal properties. In so doing, we solve a long-standing problem of eliciting multiple, continuously valued treatment policies, using an updated framework and corresponding set of assumptions, which we term the inverse classification potential outcomes framework (ICPOF), along with a new measure, referred to as the individual future estimated effects ($i$FEE). We also develop the approximate propensity score (APS), based on Gaussian processes, to weight treatments, much like the inverse propensity score weighting used in past works. We demonstrate the viability of our methods on student performance.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGaussian ProcessesGeneral ClassificationSimilar Papers 제목 키워드 기반
Two Layers of Instability in Causal Estimation
There is a precise sense in which drawing causal inferences from observational data is hard, even when identifiability is assumed. In particular, Robins and Ritov (1997) and Robins et al. (2003) showed that causal effect…
Causal Deep Learning
We derive a set of causal deep neural networks whose architectures are a consequence of tensor (multilinear) factor analysis, a framework that facilitates forward and inverse causal inference. Forward causal questions ar…
Causal InferenceDeep LearningDimensionality ReductionWhat can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems
We consider basic conceptual questions concerning the relationship between statistical estimation and causal inference. Firstly, we show how to translate causal inference problems into an abstract statistical formalism w…
Causal InferenceFighting Spurious Correlations in Text Classification via a Causal Learning Perspective
In text classification tasks, models often rely on spurious correlations for predictions, incorrectly associating irrelevant features with the target labels. This issue limits the robustness and generalization of models,…
counterfactualCounterfactual Reasoningfeature selectiontext-classification+1On "A General Framework for Pricing Asian Options Under Markov Processes"
Cai, Song and Kou (2015) [Cai, N., Y. Song, S. Kou (2015) A general framework for pricing Asian options under Markov processes. Oper. Res. 63(3): 540-554] made a breakthrough by proposing a general framework for pricing …