[ { "Title": "One for the World — General Support", "URL": "https://www.givewell.org/about/impact/one-for-the-world/july-2018-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

For this grant, we are recording the following forecasts:

Confidence Prediction By Time
25% OFTW moves more than $2.5 million to GiveWell top charities in 2020. End of 2020
15% Conditioned on it still being active, OFTW moves more than $5 million to GiveWell top charities in 2023. End of 2023
75% We renew our support to OFTW after one year. September 2019
50% We renew our support to OFTW after two years. September 2020
", "Stars": "★★★☆☆" }, { "Title": "Georgetown University Initiative on Innovation, Development, and", "URL": "https://www.givewell.org/charities/gui2de/january-2017-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

We are experimenting with recording explicit numerical forecasts of the probability of events related to our decision-making (especially grant-making). The idea behind this is to pull out the implicit predictions that are playing a role in our decisions, and to make it possible for us to look back on how well-calibrated and accurate those predictions were. For this grant, Josh Rosenberg, our senior research analyst who led GiveWell's investigation of Zusha!, records the following forecasts:

", "Stars": "★★★☆☆" }, { "Title": "Charity Science: Health — General Support", "URL": "https://www.givewell.org/charities/charity-science/charity-science-health/november-2016-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

We are experimenting with recording explicit numerical forecasts of the probability of events related to our decision-making (especially grant-making). The idea behind this is to pull out the implicit predictions that are playing a role in our decisions, and to make it possible for us to look back on how well-calibrated and accurate those predictions were. For this grant, we are recording the following forecasts:

", "Stars": "★★★☆☆" }, { "Title": "Charity Science Health — SMS Reminders for Immunization", "URL": "https://www.givewell.org/charities/charity-science/charity-science-health/july-2017-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

For this grant, we are recording the following forecasts:

", "Stars": "★★★☆☆" }, { "Title": "Results for Development — Childhood Pneumonia Treatment Scale-Up", "URL": "https://www.givewell.org/charities/results-for-development/may-2016-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

We are experimenting with recording explicit numerical forecasts of the probability of events related to our decision-making (especially grant-making). The idea behind this is to pull out the implicit predictions that are playing a role in our decisions, and to make it possible for us to look back on how well-calibrated and accurate those predictions were. For this grant, we are recording the following forecasts:

", "Stars": "★★★☆☆" }, { "Title": "New Incentives — General Support (November 2017)", "URL": "https://www.givewell.org/charities/new-incentives/november-2017-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

We are experimenting with recording explicit numerical forecasts of the probability of events related to our decision-making (especially grant-making). The purpose of this exercise is to record the implicit predictions that inform our decisions and to make it possible for us to look back on how well-calibrated and accurate those predictions were. For this grant, we are recording the forecasts below, all of which we consider to be fairly rough. Except where otherwise noted, the end date for all predictions is the end of 2020.

", "Stars": "★★★☆☆" }, { "Title": "New Incentives — General Support (2016)", "URL": "https://www.givewell.org/charities/new-incentives/march-2016-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

We are experimenting with recording explicit numerical forecasts of the probability of events related to our decision-making (especially grant-making). The idea behind this is to pull out the implicit predictions that are playing a role in our decisions, and to make it possible for us to look back on how well-calibrated and accurate our past predictions were. For this grant, we are recording the following forecasts (made during our decision process):

Top charity predictions

Cost-effectiveness predictions

Charity predictions

", "Stars": "★★★☆☆" }, { "Title": "New Incentives — General Support", "URL": "https://www.givewell.org/charities/new-incentives/april-2017-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

We are experimenting with recording explicit numerical forecasts of the probability of events related to our decision-making (especially grant-making). The purpose of this exercise is to record the implicit predictions that inform our decisions, and to make it possible for us to look back on how well-calibrated and accurate those predictions were. For this grant, we are recording the following forecasts:

", "Stars": "★★★☆☆" }, { "Title": "Evidence Action — No Lean Season (December 2016 grant)", "URL": "https://www.givewell.org/charities/evidence-action/december-2016-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

We are experimenting with recording explicit numerical forecasts of the probability of events related to our decision-making (especially grant-making). The idea behind this is to pull out the implicit predictions that are playing a role in our decisions, and to make it possible for us to look back on how well-calibrated and accurate those predictions were. For this grant, we are recording the following forecast:

", "Stars": "★★★☆☆" }, { "Title": "Evidence Action — Strengthen Operations", "URL": "https://www.givewell.org/charities/evidence-action/april-2017-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

For this grant, we are recording the following forecasts:

", "Stars": "★★★☆☆" }, { "Title": "Evidence Action — No Lean Season (March 2016 grant)", "URL": "https://www.givewell.org/evidence-action/march-2016-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

We’re experimenting with recording explicit numerical forecasts of events related to our decisionmaking (especially grantmaking). The idea behind this is to pull out the implicit predictions that are playing a role in our decisions, and make it possible for us to look back on how well-calibrated and accurate those are. For this grant, we are recording the following forecasts:

", "Stars": "★★★☆☆" }, { "Title": "Innovations for Poverty Action — Mindset Engagement in Cash Transfers", "URL": "https://www.givewell.org/international/charities/ipa/may-2016-grant#Risks_of_the_grant_and_internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Risks of the grant and internal forecasts

This grant could fail to have the effects we hope for in a number of ways:

  1. The study detects an effect that is too small relative to the cost of implementing the intervention for it to be worth scaling up. We believe this is reasonably likely (~50% chance).
  2. The study yields a result that we're not confident in. We think there is a moderate chance (~25%) of this (given the number of potential problems that can arise with any study).
  3. The study detects an effect that would be worth scaling up, but we are unable to find an implementer interested in doing so (for instance, if GiveDirectly were to decide not to incorporate the intervention because it is too time-intensive or diverts attention from other activities, or because GiveDirectly interprets the study's results differently than we do). We think this scenario is fairly unlikely (~7.5%).
  4. The intervention has no measurable effect, and we could have predicted this prior to the study by surveying the existing literature more thoroughly. We think this is fairly unlikely (~7.5%), especially given Sedlmayr's interest in attempting the intervention.

(We’re experimenting with recording explicit numerical forecasts of events related to our decisionmaking, especially grantmaking. The idea behind this is to pull out the implicit predictions that are playing a role in our decisions, and make it possible for us to look back on how well-calibrated and accurate those are.)

", "Stars": "★★★☆☆" }, { "Title": "Evidence Action Beta — Iron and Folic Acid Supplementation ("Phase 2")", "URL": "https://www.givewell.org/research/incubation-grants/december-2018-evidence-action-beta-iron-folic-acid-phase-2#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Internal forecasts

For this grant, we are recording the following forecasts:

Confidence Prediction By Time
60% GiveWell’s best guess is that Evidence Action’s intervention increases coverage relative to the counterfactual in the first year of Phase 2 of the program by at least 4 percentage points December 2021
50% GiveWell’s best guess is that Evidence Action’s intervention increases coverage relative to the counterfactual in the second year of Phase 2 of the program by at least 8 percentage points (cumulatively) December 2021
75% Evidence Action requests funding for Phase 3 of this program because it believes Phase 2 to have been successful December 2021
80% Estimates of anemia rates from the India National Family Health Survey in an average of 5 randomly chosen non-Evidence Action-supported states do not show anemia declining by more than 2 percentage points per year over the last 5 years (e.g., due to iron fortification or other changes) January 2024
35% Evidence Action ultimately spends at least $15 million total on IFA technical assistance that we retrospectively model as 10x as effective (or more) than cash transfers (using our January 2018 CEA as a baseline) January 2025
", "Stars": "★★★☆☆" }, { "Title": "UC Berkeley — KLPS-4 Survey", "URL": "https://www.givewell.org/research/incubation-grants/uc-berkeley/april-2017-grant#Internal_forecasts", "Platform": "GiveWell", "Binary question?": false, "Percentage": "none", "Description": "

Plans for follow-up

We plan to follow up with the gift recipient roughly every six months to check in on the timeline for receiving results from this study. At this stage, our understanding is that Wave 1 results will be available by mid-2018 and Wave 2 results will be available by mid-2019. We are uncertain when results will be able to be shared publicly, but aim to write publicly about the results as soon as we are able to.

We also plan to follow up with the recipient to share their pre-analysis plan publicly and, when the study is completed, to share data publicly.

Internal forecasts

We’re experimenting with recording explicit numerical forecasts of events related to our decisionmaking (especially grantmaking). The idea behind this is to pull out the implicit predictions that are playing a role in our decisions, and make it possible for us to look back on how well-calibrated and accurate those are. For this gift, we are recording the following forecasts:

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