Explaining Classifiers with Causal Concept Effect (CaCE) (1907.07165v2)

Abstract: How can we understand classification decisions made by deep neural networks? Many existing explainability methods rely solely on correlations and fail to account for confounding, which may result in potentially misleading explanations. To overcome this problem, we define the Causal Concept Effect (CaCE) as the causal effect of (the presence or absence of) a human-interpretable concept on a deep neural net's predictions. We show that the CaCE measure can avoid errors stemming from confounding. Estimating CaCE is difficult in situations where we cannot easily simulate the do-operator. To mitigate this problem, we use a generative model, specifically a Variational AutoEncoder (VAE), to measure VAE-CaCE. In an extensive experimental analysis, we show that the VAE-CaCE is able to estimate the true concept causal effect, compared to baselines for a number of datasets including high dimensional images.

• The paper introduces Causal Concept Effect (CaCE), a novel measure quantifying the causal impact of concepts on classifier outputs to improve interpretability beyond correlation.
• The authors propose using Variational AutoEncoders (VAEs) to approximate CaCE estimation by generating counterfactual examples, effectively reducing confounding.
• Experimental results across four datasets demonstrate that the VAE-based CaCE approach provides more accurate explanations than correlation-based methods by mirroring ground-truth causal effects.

Analysis of the Causal Concept Effect (CaCE) in Classifier Interpretability

The paper "Explaining Classifiers with Causal Concept Effect (CaCE)" presents a novel approach to enhance the interpretability of classifiers by focusing on the causal impact of concepts on predictions rather than relying on correlational explanations. This method is particularly relevant in high-risk domains where understanding the decision-making process of machine learning models is crucial.

Overview of CaCE

The Causal Concept Effect (CaCE) is introduced as a quantitative measure of the causal influence of a human-interpretable concept on a classifier's output. Traditional interpretability methods often fail to account for confounding, leading to explanations that can be misleading due to correlations present in the training data. CaCE aims to discern whether the presence or absence of a concept causally affects the classifier's decisions, providing a more accurate explanation than correlation-based methods.

The paper uses the do-operator from causality theory to formalize interventions on concepts, allowing for the estimation of causal effects in a systematic and theoretically grounded manner. While theoretically appealing, direct estimation of CaCE presents challenges, especially in complex domains where interventions are difficult to simulate.

Estimation of CaCE Using VAEs

To approximate the calculation of CaCE, the authors propose leveraging generative models, specifically Variational AutoEncoders (VAEs), to generate counterfactual examples where a concept is either present or absent. The VAE-based estimation of CaCE is demonstrated to align closely with true causal effects, reducing the impact of confounding in a variety of datasets, including high-dimensional images.

Two approaches are discussed for implementing VAE-based CaCE estimation:

1. Dec-CaCE: Utilizes the generative network to sample pairs of counterfactual images differing solely in the concept of interest.
2. EncDec-CaCE: Employs both the inference and generative networks to provide explanations for specific images, useful in scenarios such as error analysis.

Experimental Findings

The experimental analysis covers four datasets: BARS, colored-MNIST, COCO-Miniplaces, and CelebA, showing how CaCE can be estimated in synthetic and real-world settings. In controlled experiments, the VAE-based approach consistently provides estimations closely mirroring ground-truth CaCE, outperforming correlation-based methods like ConExp and TCAV.

1. BARS Dataset: Demonstrates CaCE estimation where color and class are confounded, illustrating how baseline methods can be misled by correlation.
2. Colored-MNIST: Highlights how CaCE realizes the causal impact of foreground color on digit classification, with variations in dataset bias.
3. COCO-Miniplaces: Evaluating complex images, the results showcase CaCE's ability to discern object influence on scene classification.
4. CelebA: Utilizes natural confounding between hair color and gender, showing effective CaCE estimation relative to StarGAN-derived ground truth.

Implications and Future Work

The introduction of CaCE and its estimation using VAEs has significant implications for improving the robustness and reliability of classifier interpretability, especially in domains requiring causal explanations. By focusing on causality, the paper offers a path forward in reducing biases in model deployment and understanding model fairness.

Further advancements are necessary to refine generative models' ability to disentangle concepts and improve the precision of CaCE estimations. Future work might explore applications in other domains, enhance VAE architectures, and investigate automated interventions for broader applicability.

In conclusion, by emphasizing causal explanations, CaCE provides a promising framework to advance interpretability in machine learning, potentially improving trust and transparency in model predictions.