rmcelreath

set.seed(1908)
N <- 200 # number of pairs
U <- rnorm(N) # simulate confounds
# birth order and family sizes
B1 <- rbinom(N,size=1,prob=0.5) # 50% first borns
M <- rnorm( N , 2*B1 + U )
B2 <- rbinom(N,size=1,prob=0.5)
D <- rnorm( N , 2*B2 + U + 0*M ) # change the 0 to turn on causal influence of mom

rmcelreath

Data Sharing, Caring, and Access

Most major scientific journals now require data archiving after publication. Many require it for review. So anthropology must figure out how to responsibly provide access to data. Luckily, other fields have already invested a lot in figured this out.

The important thing to realize is that providing access does not mean complete loss of control, either for the communities that provide the data or for researchers. For example, access can be restricted to researchers affiliated with research institutions, and limits can be placed on rights of reuse. But some level of access is necessary, if for no other reason than to allow other scholars the ability to verify our analyses. At the same time, researchers who want access to data must respect that data are political and continued collaboration with communities that provide data requires privacy and limits on reuse.

A standard data sharing memorandum grants all collaborators non-exclusive use of the data for scientific analysis

rmcelreath

# nested varying effects in ulam

# simulate an example
# people in nations, allowing varying among people to vary by nation

N_nations <- 10
N_id_per_nation <- sample( 5:100 , size=N_nations , replace=TRUE )
N_id <- sum(N_id_per_nation)

# variation among individuals in each nation

rmcelreath

# proxy confounder problem
# cf https://twitter.com/y2silence/status/1251151157264674816

# note that I rename X1,X2 to A,B and A to X
# The DAG:
# X -> Y
# X <- U -> Y
# A <- U -> B

# Can we use A and B to deconfound X->Y?

rmcelreath

library(rstan)

# sim observations
library(gtools)
y <- rdirichlet(50,c(10,2,5))

# model
dir_model <- "
data{
    int N;

rmcelreath

# install rethinking library with instructions here:
# https://github.com/rmcelreath/statrethinking_winter2019

library(rethinking)
library(dagitty)
library(shape)

# the structural model
# W: wages
# Q: quarter of year of birth

rmcelreath

# R script to accompany EHBEA 2019 presentation "Causal Salad in Human Evolution & Ecology"

# to install latest rethinking package, see:
# https://github.com/rmcelreath/statrethinking_winter2019
library(rethinking)

# Example 1: Grandparent -> child
# what is influence of grandparent presence on grandchild welfare?
# confounded by parent condition (wealth), 
#  because grandparents live with wealthier children

rmcelreath

HMC2 <- function (U, grad_U, epsilon, L, current_q , ... ) {
  q = current_q
  p = rnorm(length(q),0,1)  # independent standard normal variates
  current_p = p
  # Make a half step for momentum at the beginning
  p = p - epsilon * grad_U( q , ... ) / 2
  # Alternate full steps for position and momentum
  qtraj <- matrix(NA,nrow=L+1,ncol=length(q))
  ptraj <- qtraj
  qtraj[1,] <- current_q

rmcelreath

library(rethinking)

HMC2 <- function (U, grad_U, epsilon, L, current_q , ... ) {
  q = current_q
  p = rnorm(length(q),0,1)  # independent standard normal variates
  current_p = p
  # Make a half step for momentum at the beginning
  p = p - epsilon * grad_U( q , ... ) / 2
  # Alternate full steps for position and momentum
  qtraj <- matrix(NA,nrow=L+1,ncol=length(q))

rmcelreath

# script to illustrate collider bias described in Breen 2018 doi:10.1093/esr/jcy037

library(rethinking)

N <- 200  # number of grandparent-parent-child triads
b_gp <- 1 # direct effect of G on P
b_gc <- 0 # direct effect of G on C
b_pc <- 1 # direct effect of P on C
b_U <- 2  # direct effect of U on P and C