NSF award 2520365 · Trustees of Boston University

Collaborative Research: Sharp Identification and Specification Testing in Potential Outcome Models: A Computational Approach

Final report due

29 December 2027

in 437 days · The award ends on 31 August 2027.

GrantClerk is a private service. We are not NSF or any government agency.

Dates this award owes

NSF emails you about this too.

Decide about an extension3 May 2027in 197 days
award ending 2027-08-31 · Research.gov
This one is ours, not NSF’s.
Extension request17 Jul 2027in 272 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2027in 307 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2027in 437 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2027in 437 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2027in 437 days
award ending 2027-08-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

Annual report dates come from the budget period on your award notice. That date is not public, so it is not shown here.

How we got this date

NSF lists this award's end date as 31 August 2027.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 2027.

Read the rule on Research.gov

Get a note before each report date

Add this award to your desk. We email you before each date and keep the dates in one place.

Renews each year until you cancel. Cancel any time from your account.

Already a member? Sign in

Award card

Award number: 2520365
Period of support: 1 Sep 2025 – 31 Aug 2027
Amount: $217,250
Awardee: Trustees of Boston University, Boston, MA
Program: Economics
Directorate: SBE

Abstract

Research using observational data and natural experiments relies on statistical analysis to provide reliable results. This project develops new methods to help data analysts test hypotheses about the causes of observed outcomes. The team improves statistical methods in a practical way that can be widely adopted by researchers, business analysts, policy analysts, and others who want to isolate the effects of changes…

Is this right?

We copy this from the public NSF award record. Your award notice and NSF always win. If a date looks wrong, check Research.gov.