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1

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Drawing causal inference from Big Data [Introductions] (2016)

Shiffrin, R. M.

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2016-07-06

Description: Human society has found the means to collect and store vast amounts of information about every subject imaginable, and is archiving this information in attempts to use it for scientific, utilitarian (e.g., health), and business purposes. These large databases are colloquially termed Big Data. How big Big Data are is...

Keywords: Sackler Colloquium on Drawing Causal Inference from Big Data

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

Published by National Academy of Sciences

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Causal inference in economics and marketing [Economic Sciences] (2016)

Varian, H. R.

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2016-07-06

Description: This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be...

Keywords: Sackler Colloquium on Drawing Causal Inference from Big Data

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

Published by National Academy of Sciences

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Modeling confounding by half-sibling regression [Computer Sciences] (2016)

Scholkopf, B., Hogg, D. W., Wang, D., Foreman–Mackey, D., Janzing, D., Simon–Gabriel, C.–J., Peters, J.

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2016-07-06

Description: We describe a method for removing the effect of confounders to reconstruct a latent quantity of interest. The method, referred to as “half-sibling regression,” is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification, discussing both independent and identically distributed as well as...

Keywords: Sackler Colloquium on Drawing Causal Inference from Big Data

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

Published by National Academy of Sciences

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Causal inference from gene perturbations [Statistics] (2016)

Meinshausen, N., Hauser, A., Mooiȷ, J. M., Peters, J., Versteeg, P., Buhlmann, P.

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2016-07-06

Description: Inferring causal effects from observational and interventional data is a highly desirable but ambitious goal. Many of the computational and statistical methods are plagued by fundamental identifiability issues, instability, and unreliable performance, especially for large-scale systems with many measured variables. We present software and provide some validation of a recently...

Keywords: Sackler Colloquium on Drawing Causal Inference from Big Data

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

Published by National Academy of Sciences

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Causal inference and the data-fusion problem [Computer Sciences] (2016)

Bareinboim, E., Pearl, J.

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2016-07-06

Description: We review concepts, principles, and tools that unify current approaches to causal analysis and attend to new challenges presented by big data. In particular, we address the problem of data fusion—piecing together multiple datasets collected under heterogeneous conditions (i.e., different populations, regimes, and sampling methods) to obtain valid answers to...

Keywords: Sackler Colloquium on Drawing Causal Inference from Big Data

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

Published by National Academy of Sciences

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Treatment pathways using the OHDSI network [Medical Sciences] (2016)

Hripcsak, G., Ryan, P. B., Duke, J. D., Shah, N. H., Park, R. W., Huser, V., Suchard, M. A., Schuemie, M. J., De; Falco, F. J., Perotte, A., Banda, J. M., Reich, C. G., Schilling, L. M., Matheny, M. E., Meeker, D., Pratt, N., Madigan, D.

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2016-07-06

Description: Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations...

Keywords: Sackler Colloquium on Drawing Causal Inference from Big Data

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

Published by National Academy of Sciences

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Lasso adjustments in randomized experiments [Statistics] (2016)

Bloniarz, A., Liu, H., Zhang, C.-H., Sekhon, J. S., Yu, B.

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2016-07-06

Description: We provide a principled way for investigators to analyze randomized experiments when the number of covariates is large. Investigators often use linear multivariate regression to analyze randomized experiments instead of simply reporting the difference of means between treatment and control groups. Their aim is to reduce the variance of the...

Keywords: Sackler Colloquium on Drawing Causal Inference from Big Data

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

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Linear mixed model for heritability estimation [Genetics] (2016)

Heckerman, D., Gurdasani, D., Kadie, C., Pomilla, C., Carstensen, T., Martin, H., Ekoru, K., Nsubuga, R. N., Ssenyomo, G., Kamali, A., Kaleebu, P., Widmer, C., Sandhu, M. S.

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2016-07-06

Description: The linear mixed model (LMM) is now routinely used to estimate heritability. Unfortunately, as we demonstrate, LMM estimates of heritability can be inflated when using a standard model. To help reduce this inflation, we used a more general LMM with two random effects—one based on genomic variants and one based...

Keywords: Sackler Colloquium on Drawing Causal Inference from Big Data

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

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Quantity discount field experiment [Economic Sciences] (2016)

Levitt, S. D., List, J. A., Neckermann, S., Nelson, D.

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2016-07-06

Description: We report on a natural field experiment on quantity discounts involving more than 14 million consumers. Implementing price reductions ranging from 9–70% for large purchases, we found remarkably little impact on revenue, either positively or negatively. There was virtually no increase in the quantity of customers making a purchase; all...

Keywords: Sackler Colloquium on Drawing Causal Inference from Big Data

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

Published by National Academy of Sciences

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Project MindScope and inferring cortical function [Neuroscience] (2016)

Hawrylycz, M., Anastassiou, C., Arkhipov, A., Berg, J., Buice, M., Cain, N., Gouwens, N. W., Gratiy, S., Iyer, R., Lee, J. H., Mihalas, S., Mitelut, C., Olsen, S., Reid, R. C., Teeter, C., de Vries, S., Waters, J., Zeng, H., Koch, C., Mind; Scope

National Academy of Sciences

In: PNAS - Proceedings of the National Academy of Sciences

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Publication Date: 2016-07-06

Description: The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function...

Keywords: Sackler Colloquium on Drawing Causal Inference from Big Data

Print ISSN: 0027-8424

Electronic ISSN: 1091-6490

Topics: Biology , Medicine , Natural Sciences in General

Published by National Academy of Sciences

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