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Attributing a deep network’s prediction to its input features

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By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN


Editor's note:Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. And yet even when the relationship between inputs and outputs is fully known and entirely deterministic, causal inference is far from obvious for a complex system. In this post, we explore causal inference in this setting via the problem of attribution in deep networks. This investigation has practical as well as philosophical implications for causal inference. On the other hand, if you just care about understanding what a deep network is doing, this post is for you too.


Deep networks have had remarkable success in variety of tasks. For instance, the…

Causality in machine learning

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By OMKAR MURALIDHARAN, NIALL CARDIN, TODD PHILLIPS, AMIR NAJMI

Given recent advances and interest in machine learning, those of us with traditional statistical training have had occasion to ponder the similarities and differences between the fields. Many of the distinctions are due to culture and tooling, but there are also differences in thinking which run deeper. Take, for instance, how each field views the provenance of the training data when building predictive models. For most of ML, the training data is a given, often presumed to be representative of the data against which the prediction model will be deployed, but not much else. With a few notable exceptions, ML abstracts away from the data generating mechanism, and hence sees the data as raw material from which predictions are to be extracted. Indeed, machine learning generally lacks the vocabulary to capture the distinction between observational data and randomized data that statistics finds crucial. To contrast machine learn…

Practical advice for analysis of large, complex data sets

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By PATRICK RILEY

For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. Some people seemed to be naturally good at doing this kind of high quality data analysis. These engineers and analysts were often described as “careful” and “methodical”. But what do those adjectives actually mean? What actions earn you these labels?

To answer those questions, I put together a document shared Google-wide which I optimistically and simply titled “Good Data Analysis.” To my surprise, this document has been read more than anything else I’ve done at Google over the last eleven years. Even four years after the last major update, I find that there are multiple Googlers with the document open any time I check.

Why has this document resonated with so many people over time? I think the main reason is that it’s full …