nhaliday + graphical-models   30

Fitting a Structural Equation Model
seems rather unrigorous: nonlinear optimization, possibility of nonconvergence, doesn't even mention local vs. global optimality...
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november 2017 by nhaliday
Does Learning to Read Improve Intelligence? A Longitudinal Multivariate Analysis in Identical Twins From Age 7 to 16
Stuart Richie, Bates, Plomin

SEM: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4354297/figure/fig03/

The variance explained by each path in the diagrams included here can be calculated by squaring its path weight. To take one example, reading differences at age 12 in the model shown in Figure​Figure33 explain 7% of intelligence differences at age 16 (.262). However, since our measures are of differences, they are likely to include substantial amounts of noise: Measurement error may produce spurious differences. To remove this error variance, we can take an estimate of the reliability of the measures (generally high, since our measures are normed, standardized tests), which indicates the variance expected purely by the reliability of the measure, and subtract it from the observed variance between twins in our sample. Correcting for reliability in this way, the effect size estimates are somewhat larger; to take the above example, the reliability-corrected effect size of age 12 reading differences on age 16 intelligence differences is around 13% of the “signal” variance. It should be noted that the age 12 reading differences themselves are influenced by many previous paths from both reading and intelligence, as illustrated in Figure​Figure33.


The present study provided compelling evidence that improvements in reading ability, themselves caused purely by the nonshared environment, may result in improvements in both verbal and nonverbal cognitive ability, and may thus be a factor increasing cognitive diversity within families (Plomin, 2011). These associations are present at least as early as age 7, and are not—to the extent we were able to test this possibility—driven by differences in reading exposure. Since reading is a potentially remediable ability, these findings have implications for reading instruction: Early remediation of reading problems might not only aid in the growth of literacy, but may also improve more general cognitive abilities that are of critical importance across the life span.

Does Reading Cause Later Intelligence? Accounting for Stability in Models of Change: http://sci-hub.tw/10.1111/cdev.12669
Results from a state–trait model suggest that reported effects of reading ability on later intelligence may be artifacts of previously uncontrolled factors, both environmental in origin and stable during this developmental period, influencing both constructs throughout development.
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september 2017 by nhaliday
PsycARTICLES - Is education associated with improvements in general cognitive ability, or in specific skills?
Results indicated that the association of education with improved cognitive test scores is not mediated by g, but consists of direct effects on specific cognitive skills. These results suggest a decoupling of educational gains from increases in general intellectual capacity.

look at Model C for the coefficients

How much does education improve intelligence? A meta-analysis: https://psyarxiv.com/kymhp
Intelligence test scores and educational duration are positively correlated. This correlation can be interpreted in two ways: students with greater propensity for intelligence go on to complete more education, or a longer education increases intelligence. We meta-analysed three categories of quasi-experimental studies of educational effects on intelligence: those estimating education-intelligence associations after controlling for earlier intelligence, those using compulsory schooling policy changes as instrumental variables, and those using regression-discontinuity designs on school-entry age cutoffs. Across 142 effect sizes from 42 datasets involving over 600,000 participants, we found consistent evidence for beneficial effects of education on cognitive abilities, of approximately 1 to 5 IQ points for an additional year of education. Moderator analyses indicated that the effects persisted across the lifespan, and were present on all broad categories of cognitive ability studied. Education appears to be the most consistent, robust, and durable method yet to be identified for raising intelligence.

three study designs: control for prior IQ, exogenous policy change, and school age cutoff regression discontinuity

It’s surprising that there isn’t much of a fadeout (p11) – half of the effect size is still there by age 70 (?!). That wasn’t what I expected. Maybe they’re being pulled upwards by smaller outlier studies – most of the bigger ones tend towards the lower end.

These gains are hollow, as they acknowledge in the discussion. Examples:
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march 2017 by nhaliday
The genetics of politics: discovery, challenges, and progress
Figure 1. Summary of relative genetic and environmental influences on political traits.

- heritability increases discontinuously on leaving home
- pretty big range of heritability for different particular traits (party identification is lowest w/ largest shared environment by far)
- overall ideology quite highly heritable
- social trust is surprisingly highly compared other measurements I've seen...
- ethnocentrism quite low (sample-dependent?)
- authoritarianism and traditionalism quite high
- voter turnout quite high

Genes, psychological traits and civic engagement: http://rstb.royalsocietypublishing.org/content/370/1683/20150015
We show an underlying genetic contribution to an index of civic engagement (0.41), as well as for the individual acts of engagement of volunteering for community or public service activities (0.33), regularly contributing to charitable causes (0.28) and voting in elections (0.27). There are closer genetic relationships between donating and the other two activities; volunteering and voting are not genetically correlated. Further, we show that most of the correlation between civic engagement and both positive emotionality and verbal IQ can be attributed to genes that affect both traits.

Are Political Orientations Genetically Transmitted?: http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1006&context=poliscifacpub
TABLE 1. Genetic and Environmental Influences on Political Attitudes: The 28 Individual Wilson–Patterson Items

The origins of party identification and its relationship to political orientations: http://sci-hub.tw/http://www.sciencedirect.com/science/article/pii/S0191886915002470

All models showed a good overall fit (see Table 3). The data indicate that party identification is substantially heritable, with about 50% of the variation in PID attributable to additive genetic effects. Moreover, the results indicate that the non-genetic influences on party identification stem primarily from unique environmental factors rather than shared ones such as growing up in the same family. This too is not consistent with the Michigan model.

Table 3 also indicates that genetic influences explained about 50% of the variance in liberalism–conservatism. This estimate is similar to previous behavior genetic findings on political attitudes (e.g., Alford et al., 2005; Bouchard, 2004; Hatemi et al., 2014; Kandler, Bleidorn, & Riemann, 2012). The remaining variance was again due primarily to nonshared environmental influences. The latter finding indicates that the Michigan hypothesis that partisan social influences affect political orientations may have some merit, although the substantial level of heritability for this variable suggests that genetic effects also play an important role.


As Table 4 reveals, the best fitting model indicates that 100% of the genetic variance in PID is held in common with liberalism–conservatism ([aC2]/[aC2 + aPID2] = 1.00). Similarly, 73% of the environmental variation in PID is shared with liberalism–conservatism ([eC2]/[eC2 + ePID2] = .73). All told, only 13% of the total variance in PID cannot be explained by variation in liberalism–conservatism (1 [aC2 + eC2] = .13), as illustrated in Fig. 3. Since only a small proportion of the variance in PID cannot be explained by liberalism– conservatism, the findings are consistent with the hypothesis that genetic and environmental factors influence liberalism–conservatism, which in turn affects party identification. However, as discussed below, other causal scenarios cannot be ruled out.

Table 4 and Fig. 3 also show that 55% of the total variance in liberalism–conservatism cannot be accounted for by variance in PID

Fig. 3. Venn diagram mapping the common and specific variance in party
identification and liberalism–conservatism.

intuition for how you can figure out overlap of variance: look at how corr(PID, liberal-conservative) differs between MZ and DZ twin pairs, etc., fit structural equational model

p_k,i,j = r_A a_k,i,j,p + r_C c_k,i,p + r_E e_k,i,j,p (k=MZ or DZ, i=1..n_k, j=1,2, p=PID or LC value)

c_k,i,j,p = r_{C,p} c'_k,i,p + r_{C,common} c'_k,i,common (ditto)
e_k,i,j,p = r_{E,p} e'_k,i,j,p + r_{E,common} e'_k,i,j,common (ditto)

MZ twins:
a_MZ,i,j,p = r_{A,p} a'_MZ,i,p + r_{A,common} a'_MZ,i,common (i=1..n_k, j=1,2 p=PID or LC value)

DZ twins:
a_DZ,i,j,p = r_{A,p} (1/2 a'_DZ,i,p + 1/2 a'_DZ,i,j,p) + r_{A,common} (1/2 a'_DZ,i,common + 1/2 a'_DZ,i,j,common) (i=1..n_k, j=1,2 p=PID or LC value)

Gaussian distribution for the underlying a', c' and e' variables, maximum likelihood, etc.

see page 9 here: https://pinboard.in/u:nhaliday/b:70f8b5b559a9

1. calculate population means μ from data (so just numbers)
2. calculate covariance matrix Σ in terms of latent parameters r_A, r_C, etc. (so variable correlations)
3. assume observed values are Gaussian with those parameters μ, Σ
4. maximum likelihood to figure out the parameters r_A, r_C, etc.

A Genetic Basis of Economic Egalitarianism: http://sci-hub.tw/10.1007/s11211-017-0297-y
Our results show that the large portion of the variance in a four-item economic egalitarianism scale can be attributed to genetic factor. At the same time, shared environment, as a socializing factor, has no significant effect. The effect of environment seems to be fully reserved for unique personal experience. Our findings further problematize a long-standing view that social justice attitudes are dominantly determined by socialization.

published in the journal "Social Justice Research" by some Hungarians, lol

various political science findings, w/ a few behavioral genetic, focus on Trump, right-wing populism/authoritarianism, and polarization: http://www.nationalaffairs.com/blog/detail/findings-a-daily-roundup/a-bridge-too-far
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february 2017 by nhaliday
What is the difference between inference and learning? - Quora
- basically boils down to latent variables vs. (hyper-)parameters
- so computing p(x_h|x_v,θ) vs. computing p(θ|X_v)
- from a completely Bayesian perspective, no real difference
- described in more detail in [Kevin Murphy, 10.4]
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january 2017 by nhaliday
CS 731 Advanced Artificial Intelligence - Spring 2011
- statistical machine learning
- sparsity in regression
- graphical models
- exponential families
- variational methods
- dimensionality reduction, eg, PCA
- Bayesian nonparametrics
- compressive sensing, matrix completion, and Johnson-Lindenstrauss
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january 2017 by nhaliday
collider C = A->C<-B
A, B d-connected (resp. conditioned on Z) iff path A~>B or B~>A w/o colliders (resp. path excluding vertices in Z)
A,B d-separated conditioned on Z iff not d-connected conditioned on Z

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january 2017 by nhaliday
A Variant on “Statistically Controlling for Confounding Constructs is Harder than you Think”
It’s taken me some time to master this formalism, but I now find it quite easy to reason about these kinds of issues thanks to the brevity of graphical models as a notational technique. I’d love to see this approach become more popular in psychology, given that it has already become quite widespread in other fields. Of course, Westfall and Yarkoni are already advocating for something very similar by advocating for the use of SEM’s, but the graphical approach is strictly more general than SEM’s and, in my personal opinion, strictly simpler to reason about.
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may 2016 by nhaliday

bundles : academeacmframe

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