Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (1707.06422v3)

Abstract: An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ. In many cases, these different distributions can be modeled as different contexts of a single underlying system, in which each distribution corresponds to a different perturbation of the system, or in causal terms, an intervention. We focus on a class of such causal domain adaptation problems, where data for one or more source domains are given, and the task is to predict the distribution of a certain target variable from measurements of other variables in one or more target domains. We propose an approach for solving these problems that exploits causal inference and does not rely on prior knowledge of the causal graph, the type of interventions or the intervention targets. We demonstrate our approach by evaluating a possible implementation on simulated and real world data.

• The paper introduces a novel causal inference approach to identify invariant predictive features for robust domain adaptation across variable contexts.
• It employs a weak assumptions framework and the Joint Causal Inference method to exploit multiple interventions without requiring prior causal graph knowledge.
• Empirical results on synthetic and genomic datasets demonstrate reliable predictive stability even in the presence of latent confounders.

An Overview of "Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions"

The paper "Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions" addresses the significant challenge in machine learning and statistics of predicting accurately when the source domain (training data) and target domain (test data) exhibit differences in their underlying data distributions. These differences are cast within a causal inference framework, interpreting them as varying contexts of a single underlying system caused by different interventions.

Methodology and Main Contributions

The approach developed in this work does not assume prior knowledge of the causal graph or specifics about the interventions and their targets. Instead, it proposes using causal inference to predict invariant conditional distributions for domain adaptation. Specifically, it innovates by targeting causal domain adaptation problems where data from one or more source domains are used to predict distributional characteristics of a target variable across target domains. The method takes into account the presence of latent confounders and leverages the Joint Causal Inference (JCI) framework to exploit information provided by multiple interventions across different domains.

The key contributions of the paper include:

1. Weak Assumptions Framework: The authors introduce a set of relatively weak assumptions that make their domain adaptation approach broadly applicable.
2. Predictive Stability: The core idea is to find a subset of features that can predict a target variable while maintaining invariant predictive performance across different domains. This ensures transferability of predictions without being affected by the distribution shifts that typically plague domain adaptation tasks.
3. Evaluation Methods: The proposed method is tested on both synthetic data and real-world datasets, such as genomic datasets, to demonstrate its efficacy in accurately predicting phenotypic measurements.
4. Algorithmic Approach: A brute-force algorithm is developed to identify the optimal subset of invariant predictive features, utilizing an automated theorem prover to verify separating sets of variables based on their causal relationships.

Numerical Results and Insights

The numerical experiments reveal the efficacy of using causal inference for domain adaptation. Notably, when there are significant causal relationships that shift conditional distributions between domains, traditional predictive models fail to adapt successfully. By identifying invariant causal paths, the proposed method mitigates these shifts and provides more stable predictions.

Implications and Future Directions

Practically, this approach offers a robust pathway for tasks where distribution shifts between data regimes can critically alter predictive performance, such as in genomics and personalized medicine. Theoretically, it opens discussions on the necessity of understanding joint causal structures across different contexts to tackle domain shifts.

Future research could explore the scalability of the method to larger datasets with more complex causal graphs. Enhancing causal discovery algorithms, especially in the presence of latent confounders and cyclic dependencies, might provide even greater prediction accuracy and domain adaptability. Additionally, expanding to non-linear causal models could further generalize the application scope of this method.

In conclusion, the paper provides a novel intersection of causal inference and machine learning for domain adaptation, a critical contribution to addressing one of the enduring problems in predictive modeling across varied contexts.