Applications closed

2021 Statistics for Improving Insights, Models, and Decisions request for proposals


At Facebook, our research teams strive to improve decision making for a business that touches the lives of billions of people across the globe. Making advances in data science methodologies helps us make the best decisions for our community, products, and infrastructure.

This year at the virtual Web Conference, we are continuing our success from the 2020 Statistics for Improving Insights and Decisions research awards to foster further innovation in this area and to deepen our collaborations with academia. Facebook is pleased to invite faculty to respond to this call for research proposals pertaining to the below topics. We anticipate awarding a total of five awards, each in the $50,000 range. Payment will be made to the proposer’s host university as an unrestricted gift. In addition, PIs and Co-PIs on the winning proposals will be automatically granted access to CrowdTangle, a public insights tool from Facebook that makes it easy to follow, analyze, and report on what’s happening with public content on social media. Learn more about CrowdTangle here.

Award Recipients

University of Queensland

Mahsa Baktashmotlagh

Massachusetts Institute of Technology

Michael Carbin

University of California Los Angeles

Suhas Diggavi

University of Chicago

Claire Donnat

Johns Hopkins University

Ryan Huang

University of Illinois Urbana-Champaign

Bo Li

Stanford University

Ayfer Ozgur

Georgia Institute of Technology

B. Aditya Prakash

University of Michigan

Ambuj Tewari

Stanford University

Stefan Wager

Applications Are Currently CLosed

Application Timeline

Launch Date

April 21, 2021


June 30, 2021

Winners Announced

August 2021

Areas of Interest

Areas of interest include, but are not limited to, the following.

1. Learning and evaluation under uncertainty

  • To protect people online and provide them with a meaningful experience, Facebook develops predictive models, which typically require training and evaluation data. Obtaining this data is oftentimes resource intensive, e.g., manual labeling, surveys and product interactions. It is also subject to noise and biases. We are interested in practical methodologies that address these challenges, estimate various biases, reduce the resources used for obtaining labels and produce calibrated estimations and predictions across cohorts of varying sizes.

2. Statistical models of complex social processes

  • Facebook’s products help connect billions of people, and we often think of our products and systems as time-varying networks at scale. As these networks of connections evolve, various social processes also unfold on top of them: content spreads, social groups form and dissolve, people leverage their networks to organize events and support charitable causes, etc. Statistical models, both of the evolution of connection networks and of social processes on top of those networks, provide important input into Facebook’s efforts to design better products and to build safer and more meaningful communities. We invite proposals around the development, inference, and validation of such statistical models.
  • Design and analysis of experiments – Facebook uses frameworks for randomized experiments to measure the benefits of the improvements we make to our products. We seek to maximize what we learn from these experiments by improving how they are designed and analyzed. We are interested in methodologies which allow us to extend or enhance the standard experimentation framework: variance reduction; measuring heterogenous or time-varying effects; estimating effects of many-valued or continuous-valued treatments; aggregating information across multiple related experiments; and correcting for selection bias when randomization is imperfect. We are also actively interested in research on adaptive experimentation such as Bayesian optimization and reinforcement learning.

3. Causal inference with observational data

  • Often researchers at Facebook would like to answer causal questions even when it is not possible to conduct product tests. For instance, we may want to measure the effects of external events or understand the potential causes for anomalies that we observe in our data. Proposals in this area should improve our ability to suggest potential hypotheses for interesting phenomena or to credibly estimate the effects of known causes. Another application of interest is enabling FB to predict how key app performance and reliability metrics will change when upgrades are rolled out to the entire user base based on the treatment effect observed on a selected population during the test phase of the app. We are also interested in the related field of survey methodology – dealing with non-response or missing data.

4. Algorithmic Auditing

  • Modeling and measuring feedback loop effects in ranking and recommender systems – ML systems make predictions that get reinforced by user feedback to obtain an accurate model of the user preferences. However, this feedback loop can influence user decisions, and narrow their interests, potentially resulting in outcomes that are suboptimal. We are interested in research that sheds light on identifying these feedback loops, and model the effects they may have on preferences amplification through theoretical or empirical techniques.
  • Interpretability techniques for AI models – AI models have become increasingly complex, so it is important for both the AI practitioners and business stakeholders to have comprehensive evaluation and understanding of the AI algorithms. This can help simplify model development, and make sure we leverage AI responsibly. We are interested in interpretability techniques in (but not limited to) any of the following topics: feature attributions, aggregate attributions, feature interaction, accumulated local effects, and global/local surrogate.

5. Performance Regression Detection and Attribution

  • In large-scale distributed systems, we often set up automated alerts to surface endpoints experiencing a sustained loss in performance, and investigate their underlying root cause by analyzing combinations and sub-partitions of dozens of potentially co-dependent factors gathered from both structured and unstructured data sets. We welcome submissions on improved statistical methods for monitoring and automation in this class of problems, ranging from detecting the origin node of a fault within a networked environment to tools which can aid in improving the efficiency of general root cause analysis investigations.

6. Forecasting for Aggregated Time Series

  • Forecasting is a widely used tool for capacity planning, however it is frequently desirable to produce and analyze accurate forecasts at several layers of granularity. For example, we might want to forecast inbound traffic at the global, country, and regional level simultaneously. Emerging techniques in hierarchical forecasting and related domains offer new ways of producing forecasts which are not only consistent at different levels of aggregation, but also leverage latent information from covariance structures. We welcome submissions which extend our ability to forecast beyond well-known univariate time series methods.

7. Privacy-aware statistics for noisy, distributed data sets

  • Statistical practice at Facebook can become more complex in the context of privacy-enhancing technologies. Differential privacy (DP) involves creating noisy datasets and frameworks such as federated analytics generate insights from distributed datasets. Developing appropriate statistical methods for these situations requires careful accounting for noise and can be limited by requirements for both secure and distributed computation. We are interested in research that improves the utility of noisy datasets produced via DP, as well as new statistical methods and algorithms for federated analytics.


Proposals should include

  • A summary of the project (1-2 pages), in English, explaining the area of focus, a description of techniques, any relevant prior work, and a timeline with milestones and expected outcomes.
  • A draft budget description (1 page) including an approximate cost of the award and explanation of how funds would be spent
  • Curriculum Vitae for all project participants.
  • Organization details; this will include tax information and administrative contact details


  • Proposals must comply with applicable US and international laws, regulations and policies.
  • Applicants must be current full-time faculty at an accredited academic institution that awards research degrees to PhD students.
  • Applicants must be the Principal Investigator on any resulting award.
  • Facebook cannot consider proposals submitted, prepared or to be carried out by individuals residing in, or affiliated with an academic institution located in, a country or territory subject to comprehensive U.S. trade sanctions.
  • Government officials (excluding faculty and staff of public universities, to the extent they may be considered government officials), political figures, and politically affiliated businesses (all as determined by Facebook in its sole discretion) are not eligible.

Frequently Asked Questions

Terms & Conditions

Facebook’s decisions will be final in all matters relating to Facebook RFP solicitations, including whether or not to grant an award and the interpretation of Facebook RFP Terms and Conditions. By submitting a proposal, applicants affirm that they have read and agree to these Terms and Conditions.

  • Facebook is authorized to evaluate proposals submitted under its RFPs, to consult with outside experts, as needed, in evaluating proposals, and to grant or deny awards using criteria determined by Facebook to be appropriate and at Facebook’s sole discretion. Facebook’s decisions will be final in all matters relating to its RFPs, and applicants agree not to challenge any such decisions.
  • Facebook will not be required to treat any part of a proposal as confidential or protected by copyright, and may use, edit, modify, copy, reproduce and distribute all or a portion of the proposal in any manner for the sole purposes of administering the Facebook RFP website and evaluating the contents of the proposal.
  • Personal data submitted with a proposal, including name, mailing address, phone number, and email address of the applicant and other named researchers in the proposal may be collected, processed, stored and otherwise used by Facebook for the purposes of administering Facebook’s RFP website, evaluating the contents of the proposal, and as otherwise provided under Facebook’s Privacy Policy.
  • Neither Facebook nor the applicant is obligated to enter into a business transaction as a result of the proposal submission. Facebook is under no obligation to review or consider the proposal.
  • Feedback provided in a proposal regarding Facebook products or services will not be treated as confidential or protected by copyright, and Facebook is free to use such feedback on an unrestricted basis with no compensation to the applicant. The submission of a proposal will not result in the transfer of ownership of any IP rights.
  • Applicants represent and warrant that they have authority to submit a proposal in connection with a Facebook RFP and to grant the rights set forth herein on behalf of their organization. All awards provided by Facebook in connection with this RFP shall be used only in accordance with applicable laws and shall not be used in any way, directly or indirectly, to facilitate any act that would constitute bribery or an illegal kickback, an illegal campaign contribution, or would otherwise violate any applicable anti-corruption or political activities law.
  • Awards granted in connection with RFP proposals will be subject to terms and conditions contained in the unrestricted gift agreement (or, in some cases, other mechanisms) pursuant to which the award funding will be provided. Applicants understand and acknowledge that they will need to agree to these terms and conditions to receive an award.