| title | titleSuffix | description | services | ms.service | ms.subservice | ms.topic | ms.author | author | ms.date | ms.custom |
|---|---|---|---|---|---|---|---|---|---|---|
Make data-driven policies and influence decision making |
Azure Machine Learning |
Make data-driven decisions and policies with the Responsible AI dashboard's integration of the Causal Analysis tool EconML. |
machine-learning |
machine-learning |
enterprise-readiness |
how-to |
mesameki |
mesameki |
05/10/2022 |
responsible-ml, event-tier1-build-2022 |
While machine learning models are powerful in identifying patterns in data and making predictions, they offer little support for estimating how the real-world outcome changes in the presence of an intervention. Practitioners have become increasingly focused on using historical data to inform their future decisions and business interventions. For example, how would revenue be affected if a corporation pursues a new pricing strategy? Would a new medication improve a patient’s condition, all else equal?
The Causal Inference component of the Responsible AI dashboard addresses these questions by estimating the effect of a feature on an outcome of interest on average, across a population or a cohort and on an individual level. It also helps to construct promising interventions by simulating different feature responses to various interventions and creating rules to determine which population cohorts would benefit from a particular intervention. Collectively, these functionalities allow decision makers to apply new policies and affect real-world change.
The capabilities of this component are founded by EconML package, which estimates heterogeneous treatment effects from observational data via double machine learning technique.
Use Causal Inference when you need to:
- Identify the features that have the most direct effect on your outcome of interest.
- Decide what overall treatment policy to take to maximize real-world impact on an outcome of interest.
- Understand how individuals with certain feature values would respond to a particular treatment policy.
- The causal effects computed based on the treatment features is purely a data property. Hence, a trained model is optional when computing the causal effects.
Note
Only historic data is required to generate causal insights.
Double Machine Learning is a method for estimating (heterogeneous) treatment effects when all potential confounders/controls (factors that simultaneously had a direct effect on the treatment decision in the collected data and the observed outcome) are observed but are either too many (high-dimensional) for classical statistical approaches to be applicable or their effect on the treatment and outcome can't be satisfactorily modeled by parametric functions (non-parametric). Both latter problems can be addressed via machine learning techniques (for an example, see Chernozhukov2016).
The method reduces the problem to first estimating two predictive tasks:
- Predicting the outcome from the controls
- Predicting the treatment from the controls
Then the method combines these two predictive models in a final stage estimation to create a model of the heterogeneous treatment effect. The approach allows for arbitrary machine learning algorithms to be used for the two predictive tasks, while maintaining many favorable statistical properties related to the final model (for example, small mean squared error, asymptotic normality, construction of confidence intervals).
Project Azua provides a novel framework focusing on end-to-end causal inference. Azua’s technology DECI (deep end-to-end causal inference) is a single model that can simultaneously do causal discovery and causal inference. We only require the user to provide data, and the model can output the causal relationships among all different variables. By itself, this can provide insights into the data and enables metrics such as individual treatment effect (ITE), average treatment effect (ATE) and conditional average treatment effect (CATE) to be calculated, which can then be used to make optimal decisions. The framework is scalable for large data, both in terms of the number of variables and the number of data points; it can also handle missing data entries with mixed statistical types.
EconML (powering the backend of the Responsible AI dashboard) is a Python package that applies the power of machine learning techniques to estimate individualized causal responses from observational or experimental data. The suite of estimation methods provided in EconML represents the latest advances in causal machine learning. By incorporating individual machine learning steps into interpretable causal models, these methods improve the reliability of what-if predictions and make causal analysis quicker and easier for a broad set of users.
DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a principled four-step interface for causal inference that focuses on explicitly modeling causal assumptions and validating them as much as possible. The key feature of DoWhy is its state-of-the-art refutation API that can automatically test causal assumptions for any estimation method, thus making inference more robust and accessible to non-experts. DoWhy supports estimation of the average causal effect for backdoor, front-door, instrumental variable and other identification methods, and estimation of the conditional effect (CATE) through an integration with the EconML library.
- Learn how to generate the Responsible AI dashboard via CLIv2 and SDKv2 or studio UI
- Learn how to generate a Responsible AI scorecard) based on the insights observed in the Responsible AI dashboard.