Nature Methods: Points of Significance
Martin Krzywinski is a staff scientist at Canada’s Michael Smith Genome Sciences Centre.
Naomi Altman is a Professor of Statistics at The Pennsylvania State University.
contributing authors
Paul Blainey is an Assistant Professor of Biological Engineering at MIT and Core Member of the Broad Institute.
Danilo Bzdok is an Assistant Professor at the Department of Psychiatry, RWTH Aachen University, Germany, and a Visiting Professor at INRIA/Neurospin Saclay in France.
Kiranmoy Das is a faculty member at the Indian Statistical Institute in Kolkata, India.
Luca Greco is an Assistant Professor of Statistics at the University of Sannio in Benevento, Italy.
Jasleen Grewal is a graduate student in the Jones lab at Canada's Michael Smith Genome Sciences Centre.
Anthony Kulesa is a graduate student in the Department of Biological Engineering at MIT.
Christoph Kurz is a researcher at Novartis Pharma GmbH, Munich, Germany.
Jake Lever is a Postdoctoral Research Fellow in Bioengineering at Stanford University in Stanford, California, USA.
Geroge Luta Associate Professor of Biostatistics at the Georgetown University in Washington, DC, USA.
Jorge López Puga is a Professor of Research Methodology at UCAM Universidad Católica de Murcia.
Byran Smucker is an Associate Professor of Statistics at Miami University in Oxford, OH, USA.
Bernhard Voelkl is a Postdoctoral Research Fellow in the Division of Animal Welfare at the Veterinary Public Health Institute, University of Bern, Bern, Switzerland
Hanno Würbel is a Professor in the Division of Animal Welfare at the Veterinary Public Health Institute, University of Bern, Bern, Switzerland
Propensity score weighting
It is not certain that everything is uncertain. —Blaise Pascal
We have already explored how we can mitigate bias caused by confounding variables in observational studies using propensity score (PS) matching (PSM) and propensity score weighting (PSW). However, any statistical model is only as good as its assumptions and, if it is specified incorrectly, it can itself produce biased estimates of the treatment effect.
This month, we explore double robustness, a powerful statistical concept that provides a valuable “safety net” against the risk of an incorrect model. It offers two opportunities, instead of just one, to obtain a valid estimate of the treatment effect — making it possible to draw credible causal inferences from observational data without having to depend on a single set of modeling assumptions.
Kurz, C.F., Krzywinski, M. & Altman, N. (2026) Points of significance: Double Robustness. Nat. Methods 23:868–869.
Nature Biotechnology cover
My cover design on the 7 April 2026 Nature Biotechnology issue shows the dendrogram that represents a cluster of uniquely expressed (or downregulated) genes in human naive stem cells induced from such cells. Within each dendrogram block, the genomic barcode sequence (sampled from Supplementary Table 1) is depicted with a Code 39 barcode. The highlighted barcode is one of those used for cell isolation.
Ishiguro S. et al. A multi-kingdom genetic barcoding system for precise clone isolation (2026) Nature Biotechnology 44:616–629.
Browse my gallery of cover designs.
Happy 2026 π Day—
Art for the 5%
Celebrate π Day (March 14th) and enjoy the art — but only if you're part of the 5%.
Go ahead, see what you can't see.
Ishihara's Tests for Colour Deficiency
Authentic and accurate images of Ishihara's test plates photographed (and lovingly color-corrected) from the 38-plate Ishihara's Tests for Colour Deficiency.
I also provide the position, size, and color of each circle on each test plate.
