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Innovations in CoEfAs (e.g., GiveWell)
  • Cost-Effect-Analysis: Quant. uncertainty, transparent, customize
  • Organization and introduction
    • Using this resource
    • Key writings and resources
    • Who is involved?
    • Opportunities to contribute to this project
  • Innovations and issues
    • Limitations of GiveWell
      • (Possible errors and misunderstandings: examples from GW and beyond)
    • Incorporating uncertainty
    • (User-input, sensitivity checks)
    • (Type checking and code)
  • Tools and examples
    • Givewell models in explained maths
    • Squiggle
    • Causal.app
      • Causal/Givewell -- working examples (in progress)
    • Guesstimate
    • Pedant
    • hesim and other R package
    • cole_haus modeling
    • Other examples (MC/Fermi)
  • GiveWell model (and extensions)
    • Code representations of GW models
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On this page
  • Trialing Excel alternatives for GiveWell CEAs
  • Improving Pedant
  • Guessing uncertainty in GiveWell estimates
  1. Organization and introduction

Opportunities to contribute to this project

Here we list ways that you can contribute if you wish to this project.

PreviousWho is involved?NextLimitations of GiveWell

Last updated 3 years ago

Trialing Excel alternatives for GiveWell CEAs

One of the first steps in this project is to trial out using different interfaces for CEAs. This could help determine whether different interfaces could be valuable for representation.

Currently, Sam Nolan has created a primitive representation of the GiveDirectly model inside Causal:

There are a number of limitations to the above, that could use improvement, notably:

  • Formatting: The documentation of the parameters only fits on one line and takes up too much horizontal space, this can be fixed by using Freeform charts.

  • Calibrating the uncertainty: The uncertainty on all the parameters is guessed quite vaguely, and could be improved with careful consideration

    • Connecting this to empirical measures of uncertainty (challenging), and to previous and related work

    • Considering the plausibility of features of the implied distributions

      • e.g., something like 'this distribution implies a 10% chance of an average household size of 10 or more among those getting GiveDirectly direct transfers; this seems too high, as only 0.5% of the poorest households in Sub-Saharan have more than 7 members)

  • Further interventions for comparison: It would be great to also include/try creating AMF and DtW versions of this,

    • to show rich comparisons between different interventions,

    • to show that this can be extended to more intricate models involving health outcomes.

  • Testing other tools: In other work, it would be great if another representation could be done in Guesstimate for comparison (and perhaps code-based tools as well)

Improving Pedant

Guessing uncertainty in GiveWell estimates

Currently, as far as I am aware, no one is yet to attempt to properly guess the amount of uncertainty in each of the parameters in GiveWell's model. I believe cole_haus modelingis the closest thing to it. Representing a GiveWell CEA (or several) with uncertainty bounds in any form would be valuable.

Pedant offers an exploration of code like languages to represent CEAs. If you wish to help develop Pedant, lo, as well as get in contact with Sam Nolan on the EA Forum, Discord, or the EA Public Interest Technologists Slack. I am currently looking for people to transcribe other models, as well as Haskell Developers.

ok at open issues on GitHub
LogoGiveDirectly - Causal
GiveDirectly model within Causal