After participants had completed the initial survey, which took most of them at least 10 minutes, we measured their volunteering behaviour by asking them whether they would be willing to fill in a ‘a few extra questions’ for charity (59.3% responded yes) rather than skipping directly to the final questions. The participants were informed that their choice would be completely anonymous and that 5 SEK (around 0.50 euro or 50 US cents) would be donated to a charity organization of their choosing if they would fill out the additional questions.

Given the conjugacy of the beta for the binomial,

The statistical hypothesis we wish to investigate is whether the proportion of left-handed men is greater than, equal to, or less than the proportion of left-handed women.

Proportion of Funding Available for Program

Transfers as a percentage of total costs. GiveDirectly has other costs! So how much of our money is going to people in need? The value is derived here: https://docs.google.com/spreadsheets/d/1L03SQuAeRRfjyuxy20QIJByOx6PEzyJ-x4edz2fSiQ4/edit#gid=537899494 This is calculated by finding the average proportion over the years. TODO: Create a predictive model, fitting a normal and a beta distribution to financials Cell: B5 Units: Unitless (percentage), 0-100%

Arbitrary Donation Size, chosen to be $100,000 by GiveWell

he current results are telling us more about the structure of the model than about the world. For real results, go try the Jupyter notebook!

minimally

Direct cash transfers

Probability distributions of value per dollar for GiveWell’s top charities

Instead, what I have done is uniformly taken GiveWell’s best guess and added and subtracted 20%. These upper and lower bounds then become the 90% confidence interval of a log-normal distribution2. For example, if GiveWell’s best guess for a parameter is 0.1, I used a log-normal with a 90% CI from 0.08 to 0.12.

But how do we usefully express probabilities over rankings

References

SettingsShow All Code

2.2 Multiple lines plot

2.1 Simple line plot

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p <- ggplot(economics, aes(x = date, y = unemploy)) +

https://stats.idre.ucla.edu/spss/seminars/introduction-to-factor-analysis/a-practical-introduction-to-factor-analysis/

There are several rotation methods.

set number of participants

The reason for that is that these rules only involve one of two factors.

However, if you believe there is some latent construct that defines the interrelationship among items, then factor analysis may be more appropriate.

makes up common variance

your set of items perfectly.

assumes that there common variances takes up all of total variance, common factor analysis assumes that total variance can be partitioned into common and unique variance.

It conceptualizes constructs as causally determined by the observations (Edwards & Bagozzi, 2000). This is likely not what you want because this means that “principal component scores are”caused” by their indicators in much the same way that sumscores are “caused” by item scores

This means that the number of underlying factors is determined mostly empirically, rather than theoretically.

the latent factors

If the elasticity of substitution between capital and labor is bounded above 1, capital and labor are “gross substitutes”. In this case, any given level of output can be achieved with enough capital or enough labor; neither individual production factor is necessary. If we just have a high enough saving rate and pile up ever more capital—factories producing factories—output will rise indefinitely.

To enable them, you’ll need to first tell Git that your local branch has a remote equivalent: git push --set-upstream origin <branch-name>

subjective scales are best understood as cardinally comparable unless, and until, other evidence suggests otherwise.

how such tests could be used to ‘correct’ the data if people do not intuitively interpret subjective scales in the way hypothesised

In each case, there is evidence indicating the condition does hold and no strong evidence suggesting it does not.

(different individuals use the scale the same way and the scale end-points represent the real limits)

(each reported unit change represents the same change in magnitude)

The cardinality thesis could fail to be exactly true, but nevertheless be approximately true, such that it is unproblematic to treat it as true. 

However, all the other conditions can fail by degree and what’s important is by how much they deviate.

C1: phenomenal cardinality(the underlying subjective state, e.g. happiness, is felt in units)

Further, they interpret the scale as linear, so each point represents the same change in quantity.

scale interpretation should be understood as a search for a ‘focal point’ (or ‘Schelling point’), a default solution chosen in the absence of communication (Schelling, 1960).

too theoretical for social science.

eir decision (Kristoffersen, 2011). Thi

psychologists less so (Ferrer‐i‐Carbonell and Frijters, 2004). Despite

t would not be possible to use subjective scales to say what would increase overall happiness. 

Finally, we do not detect a significant spillover effect, that is an effect on non-recipients in other households in the same community. This result is important as a potential concern about cash transfers

A dollar to AMF is worth 10 times that of a dollar to GiveDirectly,

GWWC (already donated to top charities)fn-13 6x The Life You Can Savefn-14

this can be achieved by increasing η above and beyond the experimentally determined rate (e.g. by adding 1 to it).

Group Annual Consumption η = 1 η = 2 Median US incomefn-4 $21,000 1× 1× US poverty linefn-5 $6,000 3.5× 12× Mean income in Kenyafn-6 $1,400 15× 230× World Bank’s international poverty linefn-7 $230 91× 8,300x GiveDirectly’s average recipientsfn-8 $180 120× 14,000×         Table 1. Some key consumption levels.

how much more utility

To leverage parochialism, interventions should aim to broaden people's moral circle and

To leverage conformity, effective charities should be set as the default,

[40] or any benefits at all [34].

A series of studies by Montealegre and colleagues [36] showed that donors who are driven by effectiveness concerns (rather than emotional concerns) were rated as less moral and less desirable partners.

ate when they believe, or are explicitly told, that most others have also donated [48, 49, 50]

Altruism that is driven by emotions is perceived as more genuine [41].

Conformity

see also [39])

Although altruism is generally rewarded, several studies suggest that effective altruism is not [6,36,37].

d selected as leaders [33].

Parochialism also biases cost-benefit calculations that could lead to effective giving.

K.F. Law, D. Campbell, B. GaesserBiased benevolence: The perceived morality of effective altruism across social distancePersonal Soc Psychol Bull (2021), Article 014616722110027

approximately two thousand cases of trachoma—a bacterial infection of the eye that can lead to permanent blindness [4].

Looking at the OLS plot, we get the impression that we've identified certain text messages that outperform others. The Bayesian plots show us that this perception is incorrect. According to the Bayesian models, we have almost no idea which messages are better than others.

Imagine we randomly select one text message and put the data for that treatment in a locked box. What should our prior belief about the effect of this text message be? Empirical Bayes says, roughly, that our prior belief about the effect of the message we locked in the box should be the average effect of the other 18. We can also use the variability in the effects of the other 18 messages to tell us how confident we should be in our prior, giving us a prior distribution.

We can apply the same logic to the flu study. Imagine we randomly select one text message and put the data for that treatment in a locked box. What should our prior belief about the effect of this text message be? Empirical Bayes says, roughly, that our prior belief about the effect of the message we locked in the box should be the average effect of the other 18. We can also use the variability in the effects of the other 18 messages to tell us how confident we should be in our prior, giving us a prior distribution.

In Bayesian terms, we've constructed a prior belief about the next season's rookies' OBP from data about this season's rookies' OBP.

That's where empirical Bayes comes to the rescue. Empirical Bayes estimates a prior based on the data

The original data aren't yet available, but we can approximately reproduce the data given what we know about the study. We know that 47,306 participants were evenly assigned to one of 19 treatments or a control condition. The outcome was binary (did the patient get a vaccine or not), and we know the vaccination rate in each treatment from the PNAS publication.

You should use Bayesian analysis when comparing 4 or more "things."

declare_sampling(S = complete_rs(N, n = 50)) +

Similarly, we can compare two designs on the basis of their diagnoses:

compare_designs(planned_design, implemented_design)

diagnose_design(designs)

diagnose_design(designs)

Our simulation and diagnosis tools can take a list of designs and simulate all of them at once, creating a column called design to keep track. For example: diagnose_design(designs)

Warning messages:
1: The argument 'estimand = ' is deprecated. Please use 'inquiry = ' instead.

diagnose_design(designs)

A designer is a function that makes designs based on a few design parameters.

redesign(design, N = c(100, 200, 300, 400, 500))

diagnose_design(simulation_df, diagnosands = study_diagnosands)

Building a design from design steps

declare_inquiry(PATE = mean(Y_Z_1 - Y_Z_0)) +

draw_estimands(design)

difference_in_means

estimator <-

measurement <-

assignment <- declare_assignment(Z = complete_ra(N, prob = 0.5))

diagnose_design(design, diagnosands = study_diagnosands)

declare_sampling(S = complete_rs(N, n = 50))

declare_model( Y_Z_0 = U, Y_Z_1 = Y_Z_0 + 0.25)

draw_data(design)

When we run this population function, we will get a different 100-unit dataset each time, as shown here.

As an example of a two-level hierarchical data structure, here is a declaration for 100 households with a random number of individuals within each household.

As a bonus, the data also includes the probability that each unit is assigned to the condition in which it is in (Z_cond),

install.packages("tidyverse")

...: Optional model-function-specific parameters. As with args, these will be quosures and can be varying().

base_recipe %>% step_corr(all_of(stations), threshold = tune())

# remove any columns with a single unique value step_zv(all_predictors()) %>%

many numeric predictors that are highly correlated

Linear Models: the absolute value of the t-statistic for each model parameter is used.

Ways to contribute

Encourage more “here’s what we aren’t doing” posts (e.g. OpenPhil posting what they don’t fund)

Make EA easier for women with children

me sort of interpersonal conflict i

Number of people who listed that reason (without prompting)

I spoke with approximately 20 people who were recommended on the basis of their knowledge about EA retention. These were mostly non-University group organizers. There was moderate agreement about the reasons people leave and stay in EA.The major reasons why people leave EA are: inability to contribute, lack of cultural fit or interpersonal conflict, major life events (moving, having a child), burnout/mental health.

Given this, CEA is evaluating alternative metrics. Our current top choice is to focus on people who use our products, instead of those who are "engaged" with EA in a more subjective sense. This allows us to analyze larger populations, improving the power of our tests.

For example, to detect a change in retention rate from 95% to 90%, we need a sample of 185 individuals.[2]

Number of people

otherwise engage with any of CEA's projects. 

remaining attendees who worked for an EA organization.

population, 50-70% of the individuals who engaged in 2020 also engaged in some way in 2021.

Results

Over the past six months, CEA has moved to unify our login systems. As of this writing, event applications, the EA Forum, and EA Funds/GWWC all use the same login system. This means that we are less likely to have issues with people using different emails.

Peter Wildeford has done the largest non-manual retention analysis I know, which looked at the percentage of people who answered the EA survey using the same email in multiple years. He found retention rates of around 27%, but cautioned that this was inaccurate due to people using different email addresses each year.

50-70% of people who engaged with CEA's projects in 2020 also engaged with one of our projects so far in 2021, using a naïve method of matching people (mostly looking at email addresses).

engagement with CEA's projects as a proxy.

29.8% is much closer to the annual retention estimate produced by Peter Wildeford based on the 2018 EA Survey

 * This is not a projection, but the retention rate observed 2019-2021. 

EAG Reattendees

Projected retentionover lifetime

We identified markers of retention that we could reliably analyze from year to year for the three cohorts. 

Employees at certain organizations connected to effective altruismRecipients of Community Building GrantsAttendees of EA Global

specify() allows you to specify the variable, or relationship between variables, that you’re interested in. hypothesize() allows you to declare the null hypothesis. generate() allows you to generate data reflecting the null hypothesis. calculate() allows you to calculate a distribution of statistics from the generated data to form the null distribution.

If this probability is below some pre-defined significance level 𝛼α\alpha, then we can reject our null hypothesis.

we start by assuming that the observed data came from some world where “nothing is going on” (i.e. the observed effect was simply due to random chance), and call this assumption our null hypothesis.

formalS4classes describe the data model and the conditional test procedures,consisting of multivariate linear statistics, univariate test statistics and a reference distribu-tion.

(−∞,−0.75)(−∞,−0.75)(-\infty,-0.75). T

To create a symbol, simply convert a string or a variable containing a string with the sym() function. Plug in these symbols anywhere dplyr expects a symbol or an expression of only one field name as a function argument using the !! operator.

To capture a quosure, simply capture an expression within the quo() function

# Capture a quosure
gear_filter <- quo(gear == 5)

# Filter our data with our quosure
mtcars %>%
  filter(!!gear_filter)

he R interpreter does not evaluate the expression before it is passed to the mutate function. Since the expression is passed in a raw format to the function, mutate can do whatever it wants with it and evaluate it however it likes

The funding overhang also created bottlenecks for people able to staff projects, and to work in supporting roles.

I’d typically prefer someone in these roles to an additional person donating $400,000–$4 million.

Personally, if given the choice between finding an extra person for one of these roles who’s a good fit or someone donating $X million per year, to think the two options were similarly valuable, X would typically need to be over three, and often over 10.

A big uncertainty here is what fraction of people will ever be interested in EA in the long term – it’s possible its appeal is very narrow, but happens to include an unusually large fraction of wealthy people. In that case, the overhang could persist much longer.

Working at an EA org is only one option, and a better estimate would aim to track the number of people ‘deployed’ in research, policy, earning to give, etc. as well.

Overall, my guess is that we’re only deploying 1–2% of the net present value of the labour of the current membership.

people only hit peak productivity around age 40–60

In terms of the level of ‘talent’ of new members, we don’t have great data.

The average age of community members is several years higher.

so most ways of measuring this growth will undercount it.

You can see some more statistics on what these people are like in the EA Survey.

GWWC members, EA Funds, Founders Pledge members, Longview Philanthropy, SFF, etc. have all grown significantly (i.e. more than doubling) in the last five years.

t could crash the price

I’ve

hen I think the stock is most relevant, since that determines how many resources will be deployed in the long term.

The CIs are not exactly identical, but very close.

the slope becomes r if x and y have identical standard deviations.

fit = lm(I(X2 * 0.5 + 0.4) ~ I(X1 * 0.5 + 0.2), D_correlation)

rbeta(1, alpha, alpha) - 1

(K - 2)/2

rlkj <- function (K, eta = 1) {

Account for a positive correlation between the growth premium and population growth, as people are more likely to move to a fast growing city

inary search is particularly useful for this. To do a binary search, you repeatedly remove half of the code until you find the bug. This is fast because, with each step, you reduce the amount of code to look through by half.

Welcome

Citr package (addin) for RStudio

im %>% mutate(sims = invoke_map(f, params, n = 10)) #> # A tibble: 3 x 3

One restriction of summarise() is that it only works with summary functions that return a single value. That means that you can’t use it with functions like quantile() that return a vector of arbitrary length:

workflow for managing many models,

broom::glance)

by_country %>% mutate(glance = map(model, broom::glance)) %>% unnest(glance)

resids

resids = map2(data, model, add_residuals)

do(map_dfr(.$fit, tidy, .id = "dataset"))

mutate(fit = flatten(pmap(.l = list(.f = funcs, .formulas = models, data = dat), .f = modelr::fit_with)))

You can also use an integer to select elements by position: x <- list(list(1, 2, 3), list(4, 5, 6), list(7, 8, 9)) x %>% map_dbl(2) #> [1] 2 5 8

map_*() uses … ([dot dot dot]) to pass along additional arguments to .f each time it’s called:

The immediate consequence of the exogeneity assumption is that the errors have mean zero: E[ε] = 0, and that the regressors are uncorrelated with the errors: E[XTε] = 0.

OLS estimation can be viewed as a projection onto the linear space spanned by the regressors. (Here each of X 1 {\displaystyle X_{1}} and X 2 {\displaystyle X_{2}} refers to a column of the data matrix.)

applicable

This is illustrated at the right.

In other words, the gradient equations at the minimum can be written as: ( y − X β ^ ) T X = 0. {\displaystyle (\mathbf {y} -X{\hat {\boldsymbol {\beta }}})^{\rm {T}}X=0.}

when y is projected orthogonally onto the linear subspace spanned by the columns of X.

. Thus, the residual vector y − Xβ

ing from the moment conditions E [ x i ( y i − x i T β ) ] = 0. {\displaystyle \mathrm {E} {\big [}\,x_{i}(y_{i}-x_{i}^{T}\beta )\,{\big ]}=0.}

In particular, this assumption implies that for any vector-function ƒ, the moment condition E[ƒ(xi)·εi] = 0 will hold.

The OLS estimator β ^ {\displaystyle {\hat {\beta }}} in this case can be interpreted as the coefficients of vector decomposition of ^y = Py along the basis of X.

In statistics and signal processing, a minimum mean square error (MMSE) estimator is an estimation method which minimizes the mean square error (MSE), which is a common measure of estimator quality, of the fitted values of a dependent variable. In the Bayesian setting, the term MMSE more specifically refers to estimation with quadratic loss function. In such case, the MMSE estimator is given by the posterior mean of the parameter to be estimated. Since the posterior mean is cumbersome to calculate, the form of the MMSE estimator is usually constrained to be within a certain class of functions. Linear MMSE estimators are a popular choice since they are easy to use, easy to calculate, and very versatile. It has given rise to many popular estimators such as the Wiener–Kolmogorov filter and Kalman filter.

save persons’ factor scores, and

Principal axis factoring is not to be confused with principal components analysis (PCA), which strictly speaking is not a type of common factor analysis because it generates components rather than factors. Unlike factors, components include the unique variances of the observed variables. This is similar to how we calculate composite scores in classical test theory (though PCA finds the best possible solution to reducing variables, with their error, into composites). PCA is appropriate when we are not interested in assuming that there is an underlying latent variable such as a construct corresponding with the components. When we assume that the factors do represent latent traits, common factor analysis is more appropriate than PCA. Because we are interested in measuring latent traits or constructs, we do not use PCA in this chapter.

library(psych) fa(r = , nfactors = , n.obs = , fm = , rotate = ) The first argument, which can replace the r =, is for the data, whether it be a data frame of the raw data or a correlation or covariance matrix.

but in order to account for correlations, the current best-practice approach is to follow Katz, Kling and Liebman (2007) in calculating bootstrapped estimates of adjusted p-values using a modification of the free step-down algorithm of Westfall and Young (1993).

Do a bit more work. Re-check that your project is still in a functional state. Commit again but this time amend your previous commit. RStudio offers a check box for “Amend previous commit” or in the shell: git commit --amend --no-edit The --no-edit part retains the current commit message of “WIP”. Don’t push! Your history now looks like this: A -- B -- C -- WIP* but the changes associated with the WIP* commit now represent your last two commits, i.e. all the accumulated changes since state C. Keep going like this. Let’s say you’ve finally achieved

git checkout issue-5 git reset HEAD^

git commit --all -m "WIP" git checkout master Then when you come back to the branch and continue your work, you need to undo the temporary commit by resetting your state. Specifically, we want a mixed reset. This is “working directory safe”, i.e. it does not affect the state of any files. But it does peel off the temporary WIP commit. Below, the reference HEAD^ says to roll the commit state back to the parent of the current commit (HEAD). git checkout issue-5 git reset HEAD^

Check the remote was cloned successfully:

Unless you use the GitHub API, most of the GitHub bits really have to be done from the browser.

. Bilder and Loughin (2007) introduceda flexible loglinear modeling approach that allows researchers to consider alternative associationstructures somewhere between SPMI and complete dependence. Within this framework, a modelunder SPMI is given aslog(μab(ij))=γij+ηWa(ij)+ηYb(ij)

asymptotic distribution is a linear combination ofindependentχ21random variables (Bilder and Loughin, 2004)

a test for simultaneous pairwise marginalindependence (SPMI), involves determining whether eachW1, . . . ,WIis pairwise independent of eachY1, . . . ,YJ. OurMI.test()function calculates their modified Pearson statistic

Examining all possible combinations of the positive/negative item responses between MRCVsis the preferred way to display and subsequently analyze MRCV data.

3 How EAs get Involved in EA

To deal with this, we organised all of the factors into six overarching categories, comprising three barriers and three facilitators: 1. Difficulties in accessing evidence (six studies) 2. Challenges in understanding the evidence (three studies) 3. Insufficient resources (six studies) 4. Knowledge sharing and ease of access (six studies) 5. Professional advisors and networks (three studies) 6. A broader definition of what counts as credible evidence and better standardisation of reporting (three studies).

hey run conjoint analysis: in which customers are offered goods with various combinations of characteristics and price – maybe a pink car with a stereo for £1,000, a pink car without a stereo for £800, a blue car for £1,100 and a blue car without a stereo for £950 – to identify how much customers value each characteristic.

et me tell you a story. Once upon a time, researcher Dean Karlan was investigating microloans to poor people in South Africa, and what encourages people to take them. He sent people flyers with various designs and offering loans at various rates and sizes. It turns out that giving consumers only one choice of loan size, rather than four, increased their take-up of loans as much as if the lender had reduced the interest rate by about 20 percent. And if the flyer features a picture of a woman, people will pay more for their loan – demand was as high as if the lender had reduced the interest rate by about a third. Nobody would say in a survey or interview that they would pay more if a flyer has a lady on it. But they do. Similarly, Nobel Laureate Daniel Kahneman reports that, empirically, people are more likely to be believe a statement if it is written in red than in green. But nobody would say that in a survey, not least because we don’t know it about ourselves.

do(cluster_summary = summary(.))

gives us the best segmentation possible.

Just like K-means and hierarchical algorithms go hand-in-hand with Euclidean distance, the Partitioning Around Medoids (PAM) algorithm goes along with the Gower distance.