Microsoft is teaching computers to understand cause and effect

AI that analyzes data to help you make decisions is set to be an increasingly big part of business tools, and the systems that do that are getting smarter with a new approach to decision optimization that Microsoft is starting to make available.

Cause and effect

Machine learning is great at extracting patterns out of large amounts of data but not necessarily good at understanding those patterns, especially in terms of what causes them. A machine learning system might learn that people buy more ice cream in hot weather, but without a common sense understanding of the world, it’s just as likely to suggest that if you want the weather to get warmer then you should buy more ice cream.

Understanding why things happen helps humans make better decisions, like a doctor picking the best treatment or a business team looking at the results of AB testing to decide which price and packaging will sell more products. There are machine learning systems that deal with causality, but so far this has mostly been restricted to research that focuses on small-scale problems rather than practical, real-world systems because it’s been hard to do.

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Deep learning, which is widely used for machine learning, needs a lot of training data, but humans can gather information and draw conclusions much more efficiently by asking questions, like a doctor asking about your symptoms, a teacher giving students a quiz, a financial advisor understanding whether a low risk or high risk investment is best for you, or a salesperson getting you to talk about what you need from a new car.

A generic medical AI system would probably take you through an exhaustive list of questions to make sure it didn’t miss anything, but if you go to the emergency room with a broken bone, it’s more useful for the doctor to ask how you broke the bone and whether you can move your fingers rather than asking about your blood type.

If we can teach an AI system how to decide what’s the best question to ask next, it can use that to gather just enough information to suggest the best decision to make.

For AI tools to help us make better decisions, they need to handle both those kinds of decisions, Cheng Zhang, a principal researcher at Microsoft, explained.

The Best Next Thing

“Say you want to judge something, or you want to get the information on how to diagnose something or classify something properly: [the way to do that] is what I call Best Next Question,” said Zhang. “But if you want to do something, you want to make things better — you want to give students new teaching material, so they can learn better, you want to give a patient a  treatment so they can get better — I call that Best Next Action. And for all of these, scalability and personalization is important.”

Put all that together, and you get efficient decision making, like the dynamic quizzes that online math tutoring service Eedi uses to find out what students understand well and what they are struggling with, so it can give them the right mix of lessons to cover the topics they need help with, rather than boring them with areas they can already handle.

The multiple choice questions have only one right answer, but the wrong answers are carefully designed to show exactly what the misunderstanding is: Is someone confusing the mean of a group of numbers for the mode or the median, or do they just not know all the steps for working out the mean?

Eedi already had the questions but it built the dynamic quizzes and personalized lesson recommendations using a decision optimization API (application programming interface) created by Zhang and her team that combines different types of machine learning to handle both kinds of decisions in what she calls end-to-end causal inferencing.

“I think we’re the first team in the world to bridge causal discovery, causal inference and deep learning together,” said Zhang. “We enable a user who has data to find out the relationship between all these different variables, like what calls what. And then we also understand their relationship: For example, how much the dose [of medicine] you gave will increase someone’s health, by how much which topic you teach will increase the student’s general understanding.

“We use deep learning to answer causal questions, suggest what’s the next best action in a really scalable way and make it real world usable.”

Businesses routinely use AB testing to guide important decisions, but that has limitations Zhang points out.

“You can only do it at a high level, not an individual level,” said Zhang. “You can get to know that for this population, in general, treatment A is better than treatment B, but you cannot say for each individual which is best.

“Sometimes it’s extremely costly and time consuming, and for some scenarios, you cannot do it at all. What we’re trying to do is replace AB testing.”

From research to no code

The API to do that, currently called Best Next Question, is available in the Azure Marketplace, but it’s in private preview, so organizations wanting to use the service in their own tools the way Eedi has need to contact Microsoft.

For data scientists and machine learning experts, the service will eventually be available either through Azure Marketplace or as an option in Azure Machine Learning or possibly as one of the packaged Cognitive Services in the same way Microsoft offers services like image recognition and translation. The name might also change to something more descriptive, like decision optimization.

Microsoft is already looking at using it for its own sales and marketing, starting with the many different partner programs it offers.

“We have so many engagement programs to help Microsoft partners to grow,” said Zhang. “But we really want to find out which type of engagement program is the treatment that helps a partner grow most. So that’s a causal question, and we also need to do it in a personalized way.”

The researchers are also talking to the Viva Learning team.

“Training is definitely a scenario we want to make personalized: We want people to get taught with the material that will help them best for their job,” said Zhang.

And if you want to use this to help you make better decisions with your own data, “We want people to have an intuitive way to use it. We don’t want people to have to be data scientists.”

The open-source ShowWhy tool that Microsoft built to make causal reasoning easier to use doesn’t yet use these new models, but it has a no-code interface, and the researchers are working with that team to build prototypes, Zhang said.

“Before the end of this year, we’re going to release a demo for the deep end-to-end causal inference,” said Zhang.

She suggests that in the longer term, business users might get the benefit of these models inside systems they already use, like Microsoft Dynamics and the Power Platform.

“For general decision-making people, they need something very visual: A no-code interface where I load data, I click a button and [I see] what are the insights,” said Zhang.

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Humans are good at thinking causally, but building the graph that shows how things are connected and what’s a cause and what’s an effect is hard. These decision optimization models build that graph for you, which fits the way people think and lets you ask what-if questions and experiment with what happens if you change different values. That’s something very natural, Zhang said.

“I feel humans fundamentally want something to help them understand ‘If I do this, what happens, if I do that, what happens,’ because that’s what aids decision making,” said Zhang.

Some years ago, she built a machine learning system for doctors to predict how patients would recover in different scenarios.

“When the doctors started to use the system they would play with it to see ‘if I do this or if I do that, what happens,’” said Zhang. “But to do that, you need a causal AI system.”

Make better decisions together

Once you have causal AI, you can build a system with two-way correction where humans teach the AI what they know about cause and effect, and the AI can check whether that’s really true.

In the U.K., schoolchildren learn about Venn diagrams in year 11. But when Zhang worked with Eedi and the Oxford University Press to find the causal relationships between different topics in mathematics, the teachers suddenly realized they’d been using Venn diagrams to make quizzes for students in years 8 and 9, long before they’d told them what a Venn diagram is.

“If we use data, we discover the causal relationship, and we show it to humans — it’s an opportunity for them to reflect and suddenly these kinds of really interesting insights show up,” said Zhang.

Making causal reasoning end to end and scalable is just a first step: There’s still a lot of work to do to make it as reliable and accurate as possible, but Zhang is excited about the potential.

“40% of jobs in our society are about decision making, and we need to make high-quality decisions,” she pointed out. “Our goal is to use AI to help decision making.”