DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models (2206.06821v2)

Abstract: We present DoWhy-GCM, an extension of the DoWhy Python library, which leverages graphical causal models. Unlike existing causality libraries, which mainly focus on effect estimation, DoWhy-GCM addresses diverse causal queries, such as identifying the root causes of outliers and distributional changes, attributing causal influences to the data generating process of each node, or diagnosis of causal structures. With DoWhy-GCM, users typically specify cause-effect relations via a causal graph, fit causal mechanisms, and pose causal queries -- all with just a few lines of code. The general documentation is available at https://www.pywhy.org/dowhy and the DoWhy-GCM specific code at https://github.com/py-why/dowhy/tree/main/dowhy/gcm.

• The paper extends the DoWhy library by integrating graphical causal models to support comprehensive causal analysis.
• The paper demonstrates advanced causal discovery and reasoning using modular DAG-based mechanisms and customizable causal queries.
• The paper highlights practical applications in root cause attribution, effect estimation, and what-if scenario analysis through user-friendly design.

Overview of DoWhy-GCM: An Extension for Graphical Causal Models

The paper introduces DoWhy-GCM, an extension to the existing DoWhy library, aimed at enhancing causal inference capabilities using graphical causal models (GCMs). Unlike other causal libraries that primarily focus on estimating the effects of interventions, DoWhy-GCM provides a framework for addressing a broader spectrum of causal questions. This allows for more comprehensive analyses such as identifying root causes of outliers, diagnosing causal structures, and performing causal structure learning.

Graphical Causal Models (GCMs)

Central to the functionality of DoWhy-GCM is the use of GCMs, a formalism pioneered by Judea Pearl. GCMs utilize directed acyclic graphs (DAGs) to represent causal relationships between variables, providing a blueprint for modeling their interactions. Each node in the graph, representing a variable, is associated with a causal mechanism that determines its data-generating process considering its parent nodes. The modularity of these mechanisms permits altering any individual process without disturbing others, thereby facilitating granular causal analysis.

Key Components and Design Principles

1. Modeling Cause-Effect Relationships: Users initialize a GCM by constructing a DAG that reflects the causal structure of the system under paper. Causal mechanisms for each node are defined, either through probabilistic models or more refined additive noise models. DoWhy-GCM allows users to infer these mechanisms from data or specify them directly if known.
2. Learning Causal Mechanisms: The parameters of the causal mechanisms are learned from observational data. The library offers flexibility by permitting user-defined mechanisms, which can incorporate domain-specific knowledge or inferred causal structures from data.
3. Causal Query Capabilities: With a learned GCM, users can perform various causal queries, such as effect estimation, root cause attribution, and what-if analysis. This broadens the scope of causal inference from merely estimating intervention effects to exploring complex causal dynamics within a system.

Functional and Modular Code Design

DoWhy-GCM follows a functional programming approach, ensuring clean, state-free interactions within its API. Key design considerations include:

• Functional Integrity: Functions operate on GCM objects without altering their states, promoting clarity and ease of use.
• Defaults and Convenience: The library provides sensible default parameters to facilitate user interaction and ease the learning curve.
• Inspection and Debugging: Components of causal models are exposed as public attributes, aiding debugging and model inspection efforts.

Functionality and Applications

The DoWhy-GCM library supports two primary operations: causal discovery and causal reasoning. In causal discovery, the library infers causal graphs based on available data and domain knowledge. Causal reasoning encompasses:

• Graph Validation: Assessing and validating the model assumptions and structure of causal graphs.
• Attribution and Effect Estimation: Discerning the origins of observed outcomes and estimating intervention effects.
• What-If Analysis: Simulating interventions and computing counterfactuals to predict potential outcomes under hypothetical scenarios.

Implications and Future Directions

DoWhy-GCM stands as a versatile tool in the domain of causal inference, extending possibilities beyond traditional effect estimation methodologies. Through its modular and flexible design, it integrates well with existing libraries, enhancing functionality and fostering community contributions. Its approach to modeling entire systems with GCMs represents a significant advancement, allowing for more nuanced insights into complex systems' causal dynamics.

Future research could explore the scalability and efficiency of DoWhy-GCM’s algorithms across larger datasets and higher-dimensional systems. As causal discovery algorithms mature, the library's capabilities in dynamically constructing and refining causal graphs from observational data will likely expand, strengthening its role as an essential tool for both theoretical exploration and practical applications in AI-driven causal analysis.