Consistent counterfactuals for deep models
WebOct 23, 2024 · As studied in [ 35, 56, 57 ], an ideal counterfactual should have the following properties: (i) the highlighted regions in the images I, I' should be discriminative of their respective classes; (ii) the counterfactual should be sensible in that the replaced regions should be semantically consistent, i.e., they correspond to the same object parts; … WebJan 1, 2024 · Counterfactuals are the most natural way of explaining model behaviour to humans. However, it has certain limitations, the most important one of which is that it only applies to classification problems. Another problem is that sometimes it provides explanations which, practically, cannot be fulfilled to reverse the decision.
Consistent counterfactuals for deep models
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WebOct 6, 2024 · This paper studies the consistency of model prediction on counterfactual examples in deep networks under small changes to initial training conditions, such as weight initialization and... WebJul 13, 2024 · Counterfactual examples are generated as follows: # Generate counterfactual examples dice_exp = exp.generate_counterfactuals (query_instance, total_CFs=4, desired_class="opposite") # Visualize counterfactual explanation dice_exp.visualize_as_dataframe () Source: Jupyter Notebook
WebDec 6, 2024 · Explaining the output of a complex machine learning (ML) model often requires approximation using a simpler model. To construct interpretable explanations that are also consistent with the original ML model, counterfactual examples — showing how the model's output changes with small perturbations to the input — have been proposed. WebOct 30, 2024 · As counterfactual examples become increasingly popular for explaining decisions of deep learning models, it is essential to understand what properties quantitative evaluation metrics do capture and equally important what they do not capture. Currently, such understanding is lacking, potentially slowing down scientific progress.
WebDec 6, 2024 · We formulate feasibility constraints in counterfactual generation into two components: 1) satisfying causal relationships between features (global); 2) accommodating user preferences (local). We …
WebCounterfactuals: Given (x;p;y) happened, how would Y ... Deep IV: Problems with 2SLS Problem: Linear models aren’t very expressive. What if we want to do causal inference with time-series? ... Amazing Property: 2SLS is consistent if h is linear even if f isn’t! Prove using orthogonality of residual and prediction. Deep IV: bias from ^p(P j ...
WebFeb 14, 2024 · Counterfactual Generative Networks. The main idea of CGNs [ 3] has already been introduced in Sect. 1. Nonetheless, to aid the understanding of our method to readers that are not familiar with the CGN architecture, we summarize its salient components in this paragraph and also provide the network diagram in Appendix Section … gis maps coWebConsistent Counterfactuals for Deep Models Emily Black · Zifan Wang · Matt Fredrikson Keywords: [ explainability ] [ consistency ] [ deep networks ] [ Abstract ] [ Visit Poster at … funny female mmorpg namesWebJun 11, 2024 · Our experimental results indicate that we can successfully train deep SCMs that are capable of all three levels of Pearl's ladder of causation: association, intervention, and counterfactuals, giving rise to a powerful new approach for answering causal questions in imaging applications and beyond. funny female orc namesWebJun 23, 2024 · This work derives a general upper bound for the costs of counterfactual explanations under predictive multiplicity, which depends on a discrepancy notion between two classifiers, which describes how differently they treat negatively predicted individuals. Counterfactual explanations are usually obtained by identifying the smallest change … funny female detective booksWebMar 11, 2024 · While recent progressive techniques are said to generate “black box” models such as deep learning (deep neural network), the relatively classical methods such as decision-tree, linear ... funny female friendship quotesWebThese do not Look Like Those: An Interpretable Deep Learning Model for Image Recognition: IEEE: Correcting neural networks based on explanations: Refining Neural Networks with Compositional Explanations: ... Semantically consistent counterfactuals: Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals: Arxiv: funny female goblin namesWebor fine-tuned. This paper studies the consistency of model prediction on counterfactual examples in deep networks under small changes to initial training conditions, such as gis maps curry county