(The same content is broken up into three posts and given a very slightly different presentation on my blog.)

Overview

GiveWell models the cost-effectiveness of its top charities. Because the input parameters are uncertain (How much moral weight should we give to increasing consumption? What is the current income of a typical GiveDirectly recipient?), the resulting cost-effectiveness estimates are also fundamentally uncertain. By performing uncertainty analysis, we get a better sense of just how uncertain the results are. Uncertainty analysis is also the first step on the route to sensitivity analysis. Sensitivity analysis reveals which input parameters each charity's cost-effectiveness estimate is most sensitive to. That kind of information helps us target future investigations (i.e. uncertainty reduction). The final step is to combine the individual charity cost-effectiveness estimates into one giant model. By performing uncertainty and sensitivity analysis on this giant model, we get a better sense of which input parameters have the most influence on the relative cost-effectiveness of GiveWell's top charities—i.e. how the charities rank against each other.

A key feature of the analysis outlined above and performed below is that it requires the analyst to specify their uncertainty over each input parameter. Because I didn't want all of the results here to reflect my idiosyncratic beliefs, I instead pretended that each input parameter is equally uncertain. This makes the results "neutral" in a certain sense, but it also means that they don't reveal much about the real world. To achieve real insight, you need to adjust the input parameters to match your beliefs. You can do that by heading over to the Jupyter notebook, editing the parameters in the second cell, and clicking "Runtime > Run all". This limitation means that all the ensuing discussion is more akin to an analysis template than a true analysis.

Uncertainty analysis of GiveWell's cost-effectiveness estimates

Section overview

GiveWell produces cost-effectiveness models of its top charities. These models take as inputs many uncertain parameters. Instead of representing those uncertain parameters with point estimates—as the cost-effectiveness analysis spreadsheet does—we can (should) represent them with probability distributions. Feeding probability distributions into the models allows us to output e

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