# EconML [**EconML**](https://www.microsoft.com/en-us/research/project/econml/) 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. It allows users to easily select the best model for their questions and implement them. **The use-cases of this library are:** 1. A/B testing: *Interpret experiments with imperfect compliance* 2. Customer Segmentation: *Estimate individualized responses to incentives* 3. Multi Investment Attribution: *Distinguish the effects of multiple outreach efforts* **Resource***:* [*Github*](https://github.com/microsoft/EconML/blob/master/notebooks/CustomerScenarios/Case%20Study%20-%20Recommendation%20AB%20Testing%20at%20An%20Online%20Travel%20Company.ipynb)\_\_ --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://akm5630.gitbook.io/understanding-causal-inference/tools-and-libraries/dowhy/econml.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.