Causal Salad in Human Evolution & Ecology — EHBEA 2019 Plenary

Causal Salad in Human Evolution & Ecology — EHBEA 2019 Plenary

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Richard McElreath

April 26, 2019
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  1. Causal Salad in Human Evolution & Ecology Richard McElreath
 @rlmcelreath

    Max Planck Institute for Evolutionary Anthropology
  2. None
  3. Mom’s RS Daughter’s RS

  4. Mom’s RS Daughter’s RS What is influence of mom on

    daughter? Include mom’s birth order in regression?
  5. Department of Human Behavior, Ecology and Culture

  6. Retire statistical significance Valentin Amrhein, Sander Greenland, Blake McShane and

    more than 800 signatories call for an end to hyped claims and the dismissal of possibly crucial effects. ILLUSTRATION BY DAVID PARKINS 2019
  7. None
  8. 2016 ASA Statement on Statistical Significance and P-Values 1. Introduction

    Increased quantification of scientific research and a prolifera- tion of large, complex datasets in recent years have expanded the scope of applications of statistical methods. This has created new avenues for scientific progress, but it also brings concerns about conclusions drawn from research data. The validity of scientific conclusions, including their reproducibility, depends on more than the statistical methods themselves. Appropriately chosen techniques, properly conducted analyses and correct interpre- tation of statistical results also play a key role in ensuring that conclusions are sound and that uncertainty surrounding them is represented properly. Underpinning many published scientific conclusions is the concept of “statistical significance,” typically assessed with an index called the p-value. While the p-value can be a use- ful statistical measure, it is commonly misused and misinter- preted. This has led to some scientific journals discouraging the use of p-values, and some scientists and statisticians recom- a proposed context is a m tions, togethe the null hypo such as no diff of a relations smaller the p bility of the d ing assumptio incompatibili or providing underlying as 2. P-values do n ied hypothes were produc Researche ment about t probability th data. The p-v rah Mayo, Michele Millar, Charles Poole, Ken Rothman, Stephen Senn, Dalene Stangl, Philip Stark and Steve Ziliak for sharing their insightful perspectives. Of special note is the following article, which is a significant contribution to the literature about p-values and statistical significance. Greenland, S., Senn, S.J., Rothman, K.J., Carlin, J.B., Poole, C., Good- man, S.N. and Altman, D.G.: “Statistical Tests, P-values, Confidence Intervals, and Power: A Guide to Misinterpretations.” Though there was disagreement on exactly what the state- ment should say, there was high agreement that the ASA should be speaking out about these matters. Let us be clear. Nothing in the ASA statement is new. Statisti- cians and others have been sounding the alarm about these mat- ters for decades, to little avail. We hoped that a statement from the world’s largest professional association of statisticians would open a fresh discussion and draw renewed and vigorous atten- tion to changing the practice of science with regards to the use of statistical inference. P- r Li r M wi r M tio r Ro ca r Se r St r St r Zi sta Refere America Lim polic Gelman, [onl n - t m e n - g - y ) - s e h - ing assumptions used to calculate the p-value hold. This incompatibility can be interpreted as casting doubt on or providing evidence against the null hypothesis or the underlying assumptions. 2. P-values do not measure the probability that the stud- ied hypothesis is true, or the probability that the data were produced by random chance alone. Researchers often wish to turn a p-value into a state- ment about the truth of a null hypothesis, or about the probability that random chance produced the observed data. The p-value is neither. It is a statement about data in relation to a specified hypothetical explanation, and is not a statement about the explanation itself. 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold. Practices that reduce data analysis or scientific infer- ence to mechanical “bright-line” rules (such as “p < 0.05”) for justifying scientific claims or conclusions can were produced by random chance alone. Researchers often wish to turn a p-value into a state- ment about the truth of a null hypothesis, or about the probability that random chance produced the observed data. The p-value is neither. It is a statement about data in relation to a specified hypothetical explanation, and is not a statement about the explanation itself. 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold. Practices that reduce data analysis or scientific infer- ence to mechanical “bright-line” rules (such as “p < 0.05”) for justifying scientific claims or conclusions can lead to erroneous beliefs and poor decision making. A conclusion does not immediately become “true” on one side of the divide and “false” on the other. Researchers should bring many contextual factors into play to derive scientific inferences, including the design of a study,
  9. Illustration: Julia Suits

  10. Causal Salad • Unconscious causal model • Vague connections between

    theory, hypotheses, statistics • No explicit logic for • Control variables • Omitted variables • Any association causal?
  11. Knowing a causal relationship means being able to accurately predict

    the consequences of an intervention. From Van Lente & Dunlavey Action Philosophers!
  12. Inconvenient Truths • Covariates create confounds • Prediction not causal

    inference • Data not enough • Reproducibility not enough
  13. Pleasing Truths • Causal inference possible in observational studies •

    Explicit causal models aid design and analysis • Algorithmic framework exists: 
 do-calculus • Don’t need best, just better
  14. Mom’s RS Daughter’s RS Include mom’s birth order in regression?

  15. Elwert 2013 Graphical Causal Models, Handbook of Causal Analysis for

    Social Research Structural Causal Models Directed acyclic graphs (DAGs) Partially directed acyclic graphs (PDAGs) 265 b Dj (t+1) Uj Hj Dj (t+1) Mi,j Mi,j Di (t) Ui Hi Di (t) ial network analysis is endogenous selection bias. Mij , marital status of woman i and man stics influencing marital choice and vital status; H, health in old age. (a) Computing the implies conditioning on Mij , which induces an association between Di and Dj even if D (b) If Di affects Dj only if i and j are married (effect modification), then the existence o n the DAG, Di !Dj and Mij !Dj . Conditioning on either one of Hi or Hj would block the nditioning on the social tie Mij and allow for the identification of the causal effect of D
  16. Structural Causal Models From Paul Hünermund (@PHuenermund)

  17. 2nd edition contains lots of DAGs xcelab.net/rm/sr2 password: tempest

  18. Elemental Confounds X Y Z

  19. Elemental Confounds X Y Z 1. The Fork

  20. Elemental Confounds X Y Z 2. The Pipe

  21. Elemental Confounds X Y Z 3. The Collider

  22. Elemental Confounds switch power light 3. The Collider

  23. Elemental Confounds switch ON power ? light OFF 3. The

    Collider
  24. Elemental Confounds newsworthy rigorous published in nature 3. The Collider

  25. Elemental Confounds X Y Z The Fork X Y Z

    The Pipe X Y Z The Collider inference = f (theory, data, statistics)
  26. Elemental Confounds • Given a DAG, can: • deduce identification

    strategy • design identification strategy • compute intervention • test some aspects of DAG • communicate assumptions X Y Z X Y Z X Y Z
  27. Mom’s RS Daughter’s RS Include mom’s birth order in regression?

  28. Kin Effects & Haunted Graphs Grandparent Child Interdisciplinary perspectives on

    grandparental investment: a journey towards causality David A Coalla,b, Sonja Hilbrandc,d, Rebecca Seare and Ralph Hertwigd aSchool of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia;bDivision of Psychiatry, School of Medicine, University of Western Australia, Crawley, WA, Australia;cDepartment of Psychology, University of Basel, Basel, Switzerland;dCenter for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany;eDepartment of Population Health, London School of Hygiene and Tropical Medicine, London, UK ABSTRACT Why do grandparents invest so heavily in their grandchildren and what impact does this investment have on families? A multitude of factors influence the roles grandparents play in their families. Here, we present an interdisciplinary perspective of grandparenting incorporating theory and research from evolutionary biology, sociology and economics. Discriminative grandparental solicitude, biological relatedness and the impact of resource availability are three phenomena used to illustrate how these perspectives, within such a multi-level approach, add value by complementing not competing with each other. Changing demographics mean there is greater demand and opportunity for ARTICLE HISTORY Received 15 November 2017 Accepted 23 January 2018 KEYWORDS Grandparents; interdisciplinary perspectives; grandparent health; grandparental investment; grandchild development CONTEMPORARY SOCIAL SCIENCE, 2018 https://doi.org/10.1080/21582041.2018.1433317
  29. Kin Effects & Haunted Graphs Grandparent Parent Child

  30. Kin Effects & Haunted Graphs Grandparent Parent Child U1

  31. Kin Effects & Haunted Graphs Grandparent Parent Child U1 U2

  32. Code at https://gist.github.com/rmcelreath Kin Effects & Haunted Graphs Grandparent Parent

    Child U1 U2 Simulate 1000 families: + + – – + +
  33. Code at https://gist.github.com/rmcelreath Kin Effects & Haunted Graphs G (Intercept)

    -1 0 1 2 3 Value child ~ grandparent Grandparent Parent Child U1 U2
  34. Code at https://gist.github.com/rmcelreath Kin Effects & Haunted Graphs P G

    (Intercept) -0.5 0.0 0.5 1.0 1.5 Value child ~ grandparent + parent G (Intercept) -1 0 1 2 3 Value child ~ grandparent
  35. Kin Effects & Haunted Graphs Grandparent Parent Child U1 U2

    P G (Intercept) -0.5 0.0 0.5 1.0 1.5 Value child ~ grandparent + parent Code at https://gist.github.com/rmcelreath
  36. No Dodging Theory • Simpson’s paradox: Reversal of association when

    covariate is added to model • Purely statistical phenomenon • Uninterpretable without a causal model P G (Intercept) -0.5 0.0 0.5 1.0 1.5 Value child ~ grandparent + parent
  37. Mom’s RS Daughter’s RS Include mom’s birth order in regression?

  38. What Can Cross-Cultural Correlations Teach Us about Human Nature? Thomas

    V. Pollet & Joshua M. Tybur & Willem E. Frankenhuis & Ian J. Rickard Published online: 5 August 2014 # Springer Science+Business Media New York 2014 Abstract Many recent evolutionary psychology and human behavioral ecology studies have tested hypotheses by examining correlations between variables measured at a group level (e.g., state, country, continent). In such analyses, variables collected for each aggregation are often taken to be representative of the individuals present within them, and relationships between such variables are presumed to reflect individual-level processes. There are multiple reasons to exercise caution when doing so, including: (1) the ecological fallacy, whereby relationships observed at the aggregate level do not accurately represent individual-level processes; (2) non-independence of data points, which violates assumptions of the inferential techniques used in null hypothesis testing; and (3) cross-cultural non-equivalence of measurement (differences in construct valid- ity between groups). We provide examples of how each of these gives rise to problems (e.g., Marlowe et al. 2008, 2011), and sexual dimorphism (Wells 20 Fig. 1 Three levels at which hypotheses can be analyzed: between groups, betw groups, and within individuals over time. Note: Interactions between levels might exi if the differences between individuals, or the developmental trajectories of individuals Re-evaluating the link between brain size and behavioural ecology in primates Lauren E. Powell1, Karin Isler2 and Robert A. Barton1 1
  39. Martin 1993 Primate Origins: Plugging the Gaps Comparative Confounds REVIEW

    ARTICLE 10 11 12 13 14 15 16 7 . - ... - -.- - - - -' : .... .. \ , . . ',' .' . , , ...... ..... \ ' ......... . , . '----.-----' Full tree 10 11 12 13 14 15 16 ......................... 30/0 Sample
  40. Comparative Confounds Brain Range

  41. Comparative Confounds Brain Range (Plasticity)

  42. Comparative Confounds Brain Range (Plasticity) Extinction

  43. Comparative Confounds Brain Range (Plasticity) Extinction

  44. Comparative Confounds Brain Range (Plasticity) Extinction + – + Only

    brainy species surviving are those who are also plastic. Code at https://gist.github.com/rmcelreath
  45. Code at https://gist.github.com/rmcelreath Comparative Confounds Brain Range (Plasticity) Extinction +

    – + brain (Intercept) 0.0 0.5 1.0 1.5 Value range ~ brain
  46. Comparative Confounds Brain Range “Phylogeny”

  47. Comparative Confounds COMMENTARY doi:10.1111/evo.12832 Irrational exuberance for resolved species trees

    Matthew W. Hahn1,2,3 and Luay Nakhleh4,5 1Department of Biology, Indiana University, Bloomington, Indiana 47405 2School of Informatics and Computing, Indiana University, Bloomington, Indiana 47405 3E-mail: mwh@indiana.edu 4Department of Computer Science, Rice University, Houston, Texas 77005 5BioSciences, Rice University, Houston, Texas 77005 Received November 15, 2015 Accepted November 30, 2015 Phylogenomics has largely succeeded in its aim of accurately inferring species trees, even when there are high levels of discordance among individual gene trees. These resolved species trees can be used to ask many questions about trait evolution, including the direction of change and number of times traits have evolved. However, the mapping of traits onto trees generally uses only a single representation of the species tree, ignoring variation in the gene trees used to construct it. Recognizing that genes underlie traits, these results imply that many traits follow topologies that are discordant with the species topology. As a consequence, standard methods for character mapping will incorrectly infer the number of times a trait has evolved. This phenomenon, dubbed “hemiplasy,” poses many problems in analyses of character evolution. Here we outline these problems, explaining where and when they are likely to occur. We offer several ways in which the possible presence of hemiplasy can be diagnosed, and discuss multiple approaches to dealing with the problems presented by underlying gene tree discordance when carrying out character mapping. Finally, we discuss the implications of hemiplasy for general phylogenetic inference, including the possible drawbacks of the widespread push for “resolved” species trees. Points of View Syst. Biol. 67(6):1091–1109, 2018 © The Author(s) 2018. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For Permissions, please email: journals.permissions@oup.com DOI:10.1093/sysbio/syy031 Advance Access publication April 25, 2018 Rethinking phylogenetic comparative methods JOSEF C. UYEDA 1,∗, ROSANA ZENIL-FERGUSON 2,3, AND MATTHEW W. PENNELL 4 1Department of Biological Sciences, Virginia Polytechnic Institute and State University, 926 West Campus Drive, Blacksburg, 2Department of Biological Sciences, University of Idaho, 875 Perimeter Drive, Moscow, ID 83844 USA; 3Department of Ecology, Evolution and Behavior, University of Minnesota, 1479 Gortner Avenue, St. Paul, MN 55108 4Department of Zoology and Biodiversity Research Centre, University of British Columbia, #4200-6700 University Blvd., Vancouver ∗Correspondence to be sent to: Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksbur E-mail: juyeda@vt.edu. Received: 21 November 2017; reviews returned: 17 March 2018; accepted: 17 April 2018 Associate Editor: Nicholas Matzke Abstract.—As a result of the process of descent with modification, closely related species tend to be similar to one a myriad different ways. In statistical terms, this means that traits measured on one species will not be independ measured on others. Since their introduction in the 1980s, phylogenetic comparative methods (PCMs) have been a solution to this problem. In this article, we argue that this way of thinking about PCMs is deeply misleading. N this sowed widespread confusion in the literature about what PCMs are doing but has led us to develop metho susceptible to the very thing we sought to build defenses against—unreplicated evolutionary events. Through Studies, we demonstrate that the susceptibility to singular events is indeed a recurring problem in comparative b ited by: YS MANUSCRIPT CATEGORY: Points of View 04 SYSTEMATIC BIOLOGY VOL. 67 A B
  48. Mom’s RS Daughter’s RS Include mom’s birth order in regression?

  49. Bette and Barbara • Simulate: • Mom (X) has no

    direct influence on daughter (Y) • X and Y confounded by common U • Birth order (B) reduces mom’s RS (X) X U Y B Code at https://gist.github.com/rmcelreath
  50. Bette and Barbara X (Intercept) -0.2 0.0 0.2 0.4 0.6

    0.8 1.0 Value AIC = 1761 Code at https://gist.github.com/rmcelreath X U Y B
  51. Bette and Barbara X (Intercept) -0.2 0.0 0.2 0.4 0.6

    0.8 1.0 Value AIC = 1761 B X (Intercept) -0.2 0.0 0.2 0.4 0.6 0.8 1.0 Value AIC = 1674 Code at https://gist.github.com/rmcelreath
  52. Model Comparison Is No Solution • Confounding variables will improve

    predictions, until we (or nature) intervene • AIC cannot identify causal relationships B X (Intercept) -0.2 0.0 0.2 0.4 0.6 0.8 1.0 Value AIC = 1674
  53. Thinking Causally, not Statistically • Can use these data to

    infer X Y • Need to use B as an instrument • Requires simultaneous equations • Right stat model depends upon causal model X U Y B
  54. Causal Salad Digested • Data are not enough • Causal

    model influences design • Causal model influences analysis • Causal model communicates assumptions • Large interdisciplinary community
  55. 2nd edition contains lots of DAGs xcelab.net/rm/sr2 password: tempest

  56. None