Contributing to the first topic requires discipline.

More might come out of it than from all your earnest strivings.

It’s questionable whether you even have the right.

and admirable practices like steelmanning.

They contain contradictions. That makes them rich enough to cope with human life, which is contradictory.

g and pushed it to be more than this

the Axial religions

moral demands

Either someone is maximizing utility, or they’re in the way.

it imposes extreme moral demands on people. Sell all your possessions and give them to the poor.

Utilitarians reduce all concerns to maximising utility. They can’t be swayed by argument, except about how to maximise utility. This makes them slightly like paperclip maximisers themselves.

hey recycle humans into paperclips

AIs which

The Effective Altruism

and turns them into numbers.

as preferences,

err on

TheRodentBirthContr

Demonstrate the potential for impact via thought experiments like The Drowning Child (although use this sparingly and cautiously, as people can be turned off by obligation framing).

13.1.1 Other lists and categorizations of tools

Approaches to overcoming the barriers and biases discussed in previous chapters.

The first intervention, surgical treatment, can’t even be seen on this scale, because it has such a small impact relative to other interventions. And the best strategy, educating high-risk groups, is estimated to be 1,400 times better than that. (It’s possible that these estimates might be inaccurate, or might not capture all of the relevant effects. But it seems likely that there are still big differences between interventions.)

I created R functions for TOST for independent t-tests, paired samples t-tests, and correlation

So my functions don’t require access to the raw data

while we are more used to think in terms of standardized effect sizes (correlations or Cohen’s d).

After choosing the SESOI, you can even design your study to have sufficient power to reject the presence of a meaningful effect.

your data set (e.g., means, standard deviations, correlations

think they should power their study, rather than the set of analyses they will conduct

This section was written by David Reinstein and Luke Arundel.

An example of this is presented in the study by Lichenstein et al., (1978).

Experiential distance describes one’s proximity to a particular situation or feeling as a result of having seen something or been a part of it. In particular, a greater experiential distance is seen to make it more difficult to imagine a particular situation or feeling, therefore making it harder to empathize with someone going through it. This could create a barrier to giving effectively as most charitable giving is motivated by empathy and sympathy for victims (Lowenstein & Small, 2007). Experiential distance explains why it is easier for an individual to feel empathy for a victim if they have personally experienced the ailment or someone close to them has. As a result, they do not have to imagine the suffering it may have caused because they have directly or indirectly physically experienced it. In other words, the experiential gap is smaller. For example, people living in wealthy nations are more likely to be affected by cancer than malaria, leading to a greater support for that cause.

predict the precisionof an iROAS estimate that a proposed experimental design will deliver.

design process featuring a statistical power analysis is a crucial step in planning of aneffective experiment.

If it is obvious from this plot that the incrementaleffects have ceased to increase beyond the end of the intervention period, there is no needto add a cooldown period, since doing so only adds more uncertainty to the estimate ofiROAS

Due to the marketing intervention, incremental cost of clicks increasedduring the test period.

shows a similar TBR Causal Effect analysis for incremental ad spend (cost ofclicks) ∆cost(t).

nd the 90%middle posterior intervals of the predicted counterfactual time series, y∗t .

Bayesian inference is usedto estimate the unknowns and to derive the posterior predictive distribution of y∗t foreach t.

The simplest possible relationship is given by the regression model,yt = α + βxt + t, t in pretest period

or TBR, data are aggregated across geos to provide one observation ofresponse metric volume at every time interval for the control and treatment groups:

matched market tests, which may compare the behavior of users in a singlecontrol region with the behavior of users in a single test region.

ime-Based Regression Framework

This appendix provides a brief introduction to the several types of software and processes used to creating websites such as Increasing Effective Charitable Giving and Researching and writing for Economics students. We aim to encourage others to participate in this collaborative work, and to spin off their own projects. If you would like to provide feedback or ask a question about these projects then using ‘hypothes.is’ is an easy way to do so (please write directly in the html and contact me at daaronr at gmail dot com to let me know you’ve done so).

10 Effect of analytical (effectiveness) information on generosity

Much of this project is being openly presented in the (in-progress) “Impact of impact treatments on giving: field experiments and synthesis” bookdown, a project organised in the dualprocess repo.

We consider a range published work (from a non-systematised literature review) including laboratory and hypothetical experiments, and non-experimental analysis of related questions (such as ‘the impact of general charitable ratings).

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) {