GitHub - JoinColony/budgetBox: Collaborative ranking and budgeting via pairwise preferences

Gitcoin - Fund What Matters

The trusted directory and reference library for Ethereum public goods funding. Discover funding mechanisms, platforms, and learn what works.

GitHub - Responsible-Dataset-Sharing/easy-dataset-share: A CLI tool that helps AI researchers share datasets responsibly.

A CLI tool that helps AI researchers share datasets responsibly. - Responsible-Dataset-Sharing/easy-dataset-share

How We Write GitHub Actions in Go · Blend Engineering: Full Stack Finance

Using Prebuilt Binaries to Minimize Build Costs

refined-github/refined-github

:octocat: Browser extension that simplifies the GitHub interface and adds useful features

Join Us At The 4th Collaborative Finance Gathering

Join us at the 4th Collaborative Finance Gathering from 21-28 June 2026 at the Commons Hub in Hirschwang an der Rax, Austria

Collaborative Causal Inference with Fair Incentives

Collaborative causal inference (CCI) aims to improve the estimation of the causal effect of treatment variables by utilizing data aggregated from multiple self-interested parties. Since their source data are valuable proprietary assets that can be costly or tedious to obtain, every party has to be incentivized to be willing to contribute to the collaboration, such as with a guaranteed fair and sufficiently valuable reward (than performing causal inference on its own). This paper presents a reward scheme designed using the unique statistical properties that are required by causal inference to guarantee certain desirable incentive criteria (e.g., fairness, benefit) for the parties based on their contributions. To achieve this, we propose a data valuation function to value parties’ data for CCI based on the distributional closeness of its resulting treatment effect estimate to that utilizing the aggregated data from all parties. Then, we show how to value the parties’ rewards fairly based on a modified variant of the Shapley value arising from our proposed data valuation for CCI. Finally, the Shapley fair rewards to the parties are realized in the form of improved, stochastically perturbed treatment effect estimates. We empirically demonstrate the effectiveness of our reward scheme using simulated and real-world datasets.

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Budget Scenario Template

Boris Mann's digital garden and personal site.

GitHub Stacked PRs

Break large changes into small, reviewable, stacked pull requests with first-class GitHub support.

GitHub Copilot · Plans & pricing

GitHub Copilot works alongside you directly in your editor, suggesting whole lines or entire functions for you.

Embracing ATProto, part 2: Tangled Knots and social coding

You thought Github was a social coding platform? Think again, and get ready to tangle! Built on atproto, tangled allows you to use your Bluesky/atproto identity on a (not quite yet) fully feldged git platform!

Heterogeneous participation and allocation skews: when is choice "worth it"?

A core ethos of the Economics and Computation (EconCS) community is that people have complex private preferences and information of which the central planner is unaware, but which an appropriately designed mechanism can uncover to improve collective decisionmaking. This ethos underlies the community's largest deployed success stories, from stable matching systems to participatory budgeting. I ask: is this choice and information aggregation ``worth it''? In particular, I discuss how such systems induce \textit{heterogeneous participation}: those already relatively advantaged are, empirically, more able to pay time costs and navigate administrative burdens imposed by the mechanisms. I draw on three case studies, including my own work -- complex democratic mechanisms, resident crowdsourcing, and school matching. I end with lessons for practice and research, challenging the community to help reduce participation heterogeneity and design and deploy mechanisms that meet a ``best of both worlds'' north star: \textit{use preferences and information from those who choose to participate, but provide a ``sufficient'' quality of service to those who do not.}