Procurement Summary
Country : Canada
Summary : For Expert Advice on the Implementation of the Machine Learning Causal…
Deadline : 30 Dec 2024
Other Information
Notice Type : Tender
TOT Ref.No.: 111782877
Document Ref. No. : 10027867
Financier : Self Financed
Purchaser Ownership : Public
Tender Value : Refer Document
Purchaser's Detail
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Login to see detailsTender Details
This requirement is for Expert advice on the implementation of the machine learning causal forest algorithms for Employment and Social Development Canada.
Description of the Requirement:
The Evaluation Directorate at Employment and Social Development Canada (ESDC) requires the services of a contractor to:
•Provide expert advice to develop and implement machine learning causal forest algorithms to estimate individualized average treatment effects, while assessing and interpreting heterogeneous treatment effects across various subgroup categories to enhance the analysis of ESDC Skills and Labour Market Program Evaluations.
•Address challenges and issues related to the implementation process, including the interpretation and the validations of the robustness of the results obtained from machine learning causal forest with those of traditional methods such as propensity score matching.
Security Requirements:
There are no security requirements associated with this solicitation.
Applicable Trade Agreements:
Further to Article 800 of the Canadian Free Trade Agreement (CFTA), CFTA does not apply to this procurement.
Proposed period of contract:
The period of the Contract is from date of Contract to March 31, 2025 inclusive.
Summary information
Notice type : Request for Proposal
Region(s) of delivery : Canada
Region of opportunity : Canada
Contract duration : 3 month(s)
Procurement method : Competitive – Open Bidding
Selection criteria : Highest Combined Rating of Technical Merit and Price
Commodity - UNSPSC Click the links below to see a list of notices associated with the UNSPSC. : 81121502 Micro economic analysis See a list of notices associated with this
Procurement Mode : 2024/12/13
Closing date and time : 2024/12/30 14:00 EST
Documents
Tender Notice
rfp100027867.pdf
ddp-100027867.pdf