Causality & Domain Adaptation

This section addresses the integration of causality into machine learning and discusses domain adaptation techniques, focusing on how models can generalize across different data distributions.

Understanding Causality In Machine Learning

This section provides an overview of causality in machine learning, highlighting the key differences between correlation and causation, the use of causal graphs, and the principles of do-calculus.

What Is Causality?

This section distinguishes between correlation and causation, explaining how causal relationships are fundamentally different from mere associations.

Causal Graphs And Dags

Causal graphs, specifically Directed Acyclic Graphs (DAGs), are utilized to represent causal relationships among variables in a structured way.

The Do-Calculus

The Do-Calculus introduces Pearl’s Do-Operator, distinguishing between interventions and observations to analyze causal effects.

Causal Inference Techniques

Causal inference techniques, including RCTs and observational studies, are crucial for determining causal relationships in various contexts.

Randomized Controlled Trials (Rcts)

Randomized Controlled Trials (RCTs) are considered the gold standard for establishing causal relationships by utilizing random assignment to mitigate confounding variables.

Observational Studies

Observational studies are research methods where the investigator observes subjects without controlling the intervention.

Causal Discovery

Causal discovery involves learning the causal structure from data using different techniques.

Applications Of Causal Learning In Ml

Causal learning in machine learning has essential applications in detecting bias, enhancing decision-making, and improving explainability across various domains.

Introduction To Domain Adaptation

Domain adaptation addresses the challenge of model performance when training and test data are drawn from different distributions.

What Is Domain Adaptation?

Domain adaptation addresses the challenges in machine learning models when training and test data are drawn from different distributions.

Types Of Domain Adaptation

This section categorizes domain adaptation into four main types based on the availability of labeled data.

Techniques For Domain Adaptation

This section introduces techniques for domain adaptation to address the mismatch between training and testing domains in machine learning.

Instance Re-Weighting

Instance re-weighting is a technique used in domain adaptation to correct for distribution mismatches between training and test datasets.

Feature Transformation

Feature transformation techniques aim to create domain-invariant representations in machine learning.

Parameter Adaptation

This section discusses parameter adaptation techniques in domain adaptation to improve model performance on new datasets.

Causality Meets Domain Adaptation

This section discusses how causal mechanisms can provide invariant predictions across different domains, highlighting the interplay between causality and domain adaptation.

Why Causality Helps

Causality helps identify stable relationships across varying domains, providing a robust foundation for machine learning models to generalize effectively.

Invariant Causal Prediction (Icp)

Invariant Causal Prediction (ICP) focuses on learning predictors that remain effective across different environments.

Causal Domain Adaptation Methods

Causal Domain Adaptation Methods explore how causal representations and counterfactual reasoning can enhance domain adaptation in machine learning.

Challenges And Future Directions

This section discusses the key challenges faced in the integration of causality and domain adaptation and outlines potential future directions for research in these areas.

Key Challenges

This section discusses the principal challenges in integrating causality with domain adaptation in machine learning, highlighting issues like identifiability of causal structure and data scarcity.

Future Directions

In this section, we explore potential future directions for advancements in causal discovery and domain adaptation.