Introducing DoWhy

The human mind has a remarkable ability to associate causes with a specific event. From the outcome of an election to an object dropping on the floor, we are constantly associating chains of events that cause a specific effect. Neuropsychology...

Using Causal Reasoning To Guide Algorithms Toward a Fairer World

ILYA SHPITSER John C. Malone Assistant Professor of Computer Science, Johns Hopkins University DANIEL MALINSKY Researcher, Johns Hopkins University   Learning algorithms, which are becoming an increasingly ubiquitous part of our lives, do precisely...

From how to why: An overview of causal inference in machine learning

Ravi Pandya Artificial intelligence is good at predicting outcomes, but how do we go one step further? Here, we discuss how AI can use causal inference and machine learning to measure the effects of multiple variables – and why it’s important for...

EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation

EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft Research with the goal to combine state-of-the-art...

Causal Inference 360

A Python package for inferring causal effects from observational data. Description Causal inference analysis enables estimating the causal effect of an intervention on some outcome from real-world non-experimental observational data. This package provides...

CausalImpact

1. Installing the package 2. Creating an example dataset 3. Running an analysis 4. Plotting the results 5. Working with dates and times 6. Printing a summary table 7. Adjusting the model 8. Using a custom model 9. FAQ 10. Further resources An R package...

Causality and Machine Learning

At Microsoft Research, our causality research spans a broad array of topics, including: using causal insights to improve machine learning methods; adapting and scaling causal methods to leverage large-scale and high-dimensional datasets; and applying...

JUDEA PEARL

For fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning. Judea Pearl created the representational and computational foundation for the processing of information under uncertainty. He...

Which Patients are in Greater Need: A counterfactual analysis with reflections on COVID-19

Scott Mueller and Judea Pearl We are pleased to introduce the writing of Professor Judea Pearl, UCLA, World Leader in AIWS Award and a Mentor of AIWS.net: I feel an obligation to share with you a couple of ideas on how AI can offer new insights and new...