Causal Salad in Human Evolution & Ecology — EHBEA 2019 Plenary

Causal Salad in
Human Evolution
& Ecology
Richard McElreath 
@rlmcelreath
Max Planck Institute for Evolutionary Anthropology

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Mom’s RS
Daughter’s RS

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Mom’s RS
Daughter’s RS
What is influence of mom on daughter?
Include mom’s birth order in regression?

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Department of Human Behavior, Ecology and Culture

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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

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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
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tio
r Ro
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r Se
r St
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sta
Refere
America
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polic
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[onl
n
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e
n
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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,

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Illustration: Julia Suits

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Causal Salad
• Unconscious causal model
• Vague connections between
theory, hypotheses, statistics
• No explicit logic for
• Control variables
• Omitted variables
• Any association causal?

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Knowing a causal relationship means
being able to accurately predict the
consequences of an intervention.
From Van Lente &
Dunlavey Action
Philosophers!

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Inconvenient Truths
• Covariates create confounds
• Prediction not causal inference
• Data not enough
• Reproducibility not enough

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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

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Mom’s RS
Daughter’s RS

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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

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Structural Causal Models
From Paul Hünermund (@PHuenermund)

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

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Elemental Confounds
X Y
Z

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Elemental Confounds
X Y
Z
1. The Fork

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Elemental Confounds
X Y
Z
2. The Pipe

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Elemental Confounds
X Y
Z
3. The Collider

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Elemental Confounds
switch power
light
3. The Collider

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

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Elemental Confounds
newsworthy rigorous
published in nature
3. The Collider

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Elemental Confounds
X Y
Z
The Fork
X Y
Z
The Pipe
X Y
Z
The Collider
inference = f (theory, data, statistics)

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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

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Mom’s RS
Daughter’s RS

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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

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Grandparent
Parent
Child

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Grandparent
Parent
Child
U1

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Grandparent
Parent
Child
U1
U2

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Code at https://gist.github.com/rmcelreath
Grandparent
Parent
Child
U1
U2
Simulate 1000 families:
+ +
–
– +
+

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G
(Intercept)
-1 0 1 2 3
Value
child ~ grandparent
Grandparent
Parent
Child
U1
U2

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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

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Grandparent
Parent
Child
U1
U2
P
G
(Intercept)
-0.5 0.0 0.5 1.0 1.5
Value

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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

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Mom’s RS
Daughter’s RS

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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

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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

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Brain Range

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Brain Range
(Plasticity)

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Brain Range
(Plasticity)
Extinction

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Brain Range
(Plasticity)
Extinction
+
–
+
Only brainy species surviving are
those who are also plastic.

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Brain Range
(Plasticity)
Extinction
+
–
+
brain
(Intercept)
0.0 0.5 1.0 1.5
Value
range ~ brain

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Brain Range
“Phylogeny”

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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: [email protected]
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: [email protected]
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: [email protected]
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

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Mom’s RS
Daughter’s RS

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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

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Bette and Barbara
X
(Intercept)
-0.2 0.0 0.2 0.4 0.6 0.8 1.0
Value
AIC = 1761
X
U
Y
B

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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

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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

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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

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Causal Salad Digested
• Data are not enough
• Causal model influences design
• Causal model influences
analysis
• Causal model communicates
assumptions
• Large interdisciplinary
community

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

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Causal Salad in Human Evolution & Ecology — EHBEA 2019 Plenary

Causal Salad in Human Evolution & Ecology — EHBEA 2019 Plenary

Richard McElreath

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