# Whittemore: An embedded domain specific language for causal programming

## TL;DR

Whittemore is a new embedded language designed for causal programming, enabling users to perform causal inference using structural causal models with simplified syntax and without deep algorithmic knowledge.

## Contribution

It introduces Whittemore, a domain-specific language that simplifies causal inference tasks based on structural causal models, with a focus on accessibility and extensibility.

## Key findings

- Successfully performs causal inference with real data
- Provides a user-friendly syntax similar to mathematical notation
- Supports future extensions for causal programming

## Abstract

This paper introduces Whittemore, a language for causal programming. Causal programming is based on the theory of structural causal models and consists of two primary operations: identification, which finds formulas that compute causal queries, and estimation, which applies formulas to transform probability distributions to other probability distribution. Causal programming provides abstractions to declare models, queries, and distributions with syntax similar to standard mathematical notation, and conducts rigorous causal inference, without requiring detailed knowledge of the underlying algorithms. Examples of causal inference with real data are provided, along with discussion of the implementation and possibilities for future extension.