NeurIPS 2018: Developments in machine learning through the lens of Counterfactual Inference [Tutorial]

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The 32nd NeurIPS Conference kicked off on the 2nd of December and continued till the 8th of December in Montreal, Canada. This conference covered tutorials, invited talks, product releases, demonstrations, presentations, and announcements related to machine learning research.

“Counterfactual Inference” is one such tutorial presented during the NeurIPS by Susan Athey, The Economics of Technology Professor at the Stanford Graduate School of Business. This tutorial reviewed the literature that brings together recent developments in machine learning with methods for counterfactual inference. It will focus on problems where the goal is to estimate the magnitude of causal effects, as well as to quantify the researcher’s uncertainty about these magnitudes.

She starts by mentioning that there are two sets of issues make causal inference must know concepts for AI. Some gaps between what we are doing in our research, and what the firms are applying. There are success stories such as Google images and so on. However, the top tech companies also do not fully adopt all the machine learning / AI concepts fully.

If a firm dumps their old simple regression credit scoring model and makes use of a black box based on ML, are they going to worry what’s going to happen when they use the Black Box algorithm?

According to Susan, the reason why firms and economists historically use simple models is that just by looking at the data it is difficult to understand whether the approach used is right. Whereas, using a Black box algorithm imparts some of the properties such as Interpretability, which helps in reasoning about the correctness of the approach. This helps researchers to make improvements in the model. Secondly, stability and robustness are also important for applications. Transfer learning helps estimate the model in one setting and use the same learning in some other setting. Also, these models will show fairness as many aspects of discrimination relates to correlation vs. causation. Finally, machine learning imparts a Human-like AI behavior that gives them the ability to make reasonable and never seen before decisions.

All of these desired properties can be obtained in a causal model.

The Causal Inference Framework

In this framework, the goal is to learn a model of how the world works. For example, what happens to a body while a drug enters. Impact of intervention can be context specific. If a user learns something in a particular setting but it isn’t working well in the other setting, it is not a problem with the framework.

It’s, however, hard to do causal inference, there are some challenges including:

  • We do not have the right kind of variation in the data.
  • Lack of quasi-experimental data for estimation
  • Unobserved contexts/confounders or insufficient data to control for observed confounders
  • Analyst’s lack of knowledge about model

Prof. Athey explains the true AI algorithm by using an example of contextual bandit under which there might be different treatments. In this example, one can select among alternative choices. They must have an explicit or implicit model of payoffs from alternatives. They also learn from past data. Here, the initial stages of learning have limited data, where there is a statistician inside the AI which performs counterfactual reasoning. A statistician should use best performing techniques (efficiency, bias).

Counterfactual Inference Approaches

Approach 1: Program Evaluation or Treatment Effect Estimation

The goal of this approach is to estimate the impact of an intervention or treatment assignment policies. This literature focuses mainly on low dimensional interventions. Here, the estimands or the things that people want to learn is the average effect (Did it work?). For more sophisticated projects, people seek the heterogeneous effect (For whom did it work?) and optimal policy (policy mapping of people’s behavior to their assignments).

The main goal here is to set confidence intervals around these effects to avoid bias or noisy sampling. This literature focuses on design that enables identification and estimation of these effects without using randomized experiments. Some of the designs include Regression discontinuity, difference-in-difference, and so on.

Approach 2: Structural Estimation or ‘Generative models and counterfactuals’

Here the goal is to impact on welfare/profits of participants in alternative counterfactual regimes. These regimes may not have ever been observed in relevant contexts. These also need a behavioral model of participants. One can make use of Dynamic structural models to learn about value function from agent choices in different states.

Approach 3: Causal discovery

The goal of this approach is to uncover the causal structure of a system. Here the analyst believes that there is an underlying structure where some variables are causes of others, e.g. a physical stimulus leads to biological responses. Application of this can be found in understanding software systems and biological systems.

Recent literature brings causal reasoning, statistical theory, and modern machine learning algorithms together to solve important problems.

The difference between supervised learning and causal inference is that supervised learning can evaluate in a test set in a model‐free way. In causal inference, parameter estimation is not observed in a test set. Also, it requires theoretical assumptions and domain knowledge.

Estimating ATE (Average Treatment Effects) under unconfoundedness

Here only the observational data is available and only an analyst has access to the data that is sufficient for the part of the information used to assign units to treatments that is related to potential outcomes.

The speaker here has used an example of how online Ads are targeted using cookies. The user sees car ads because the advertiser knows that the user has visited car reviewer websites. Here the purchases cannot be related to users who saw an ad versus the ones who did not. Hence, the interest in cars is the unobserved confounder. However, the analyst can see the history of the websites visited by the user. This is the main source of information for the advertiser about user interests.

Using Supervised ML to measure estimate ATE under unconfoundedness

The first supervised ML method is propensity score weighting or KNN on propensity score. For instance, make use of the LASSO regression model to estimate the propensity score. The second method is Regression adjustment which tries to estimate the further outcomes or access the features of further outcomes to get a causal effect. The next method is estimating CATE (Conditional average treatment effect) and take averages using the BART model. The method mentioned by Prof. Athey here is, Double robust/ double machine learning which uses cross-fitted augmented inverse propensity scores. Another method she mentioned was Residual Balancing which avoids assuming a sparse model thus allowing applications with a complex assignment.

If unconfoundedness fails, the alternate assumption: there exists an instrumental variable
Zi that is correlated with Wi (“relevance”) and where:

Structural Models

Structural models enable counterfactuals for never‐seen worlds. Combining Machine learning with structural model provides attention to identification, estimation using “good” exogenous variation in data. Also, adding a sensible structure improves performance required for never‐seen counterfactuals, increased efficiency for sparse data (e.g. longitudinal data)

Nature of structure includes:

  • Learning underlying preferences that generalize to new situations
  • Incorporating nature of choice problem
  • Many domains have established setups that perform well in data‐poor environments

With the help of Discrete Choice Model, users can evaluate the impact of a new product
introduction or the removal of a product from choice set. On combining these Discrete Choice Models with ML, we have two approaches to product interactions:

  • Use information about product categories, assume products substitutes within categories
  • Do not use available information about categories, estimate subs/complements

Susan has concluded by mentioning some of the challenges on Causal inference, which include data sufficiency, finding sufficient/useful variation in historical data. She also mentions that recent advances in computational methods in ML don’t help with this. However, tech firms conducting lots of experiments, running bandits, and interacting with humans at large scale can greatly expand the ability to learn about causal effects!

Head over to the Susan Athey’s entire tutorial on Counterfactual Inference at NeurIPS Facebook page.

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Savia Lobo
A Data science fanatic. Loves to be updated with the tech happenings around the globe. Loves singing and composing songs. Believes in putting the art in smart.

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