Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Long-term Causal Effects via Behavioral Game Theory
Authors: Panagiotis Toulis, David C. Parkes
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we apply our methodology to experimental data from Rapoport and Boebel [18], as reported by Mc Kelvey and Palfrey [15]. |
| Researcher Affiliation | Academia | Panagiotis (Panos) Toulis Econometrics & Statistics, Booth School University of Chicago Chicago, IL, 60637 EMAIL David C. Parkes Department of Computer Science Harvard University Cambridge, MA, 02138 EMAIL |
| Pseudocode | Yes | Algorithm 1 Estimation of long-term causal effects Input: Z, T, A, B, G1, G0, D1 = {a1(t; Z) : t = 0, . . . , t0}, D0 = {a0(t; Z) : t = 0, . . . , t0}. Output: Estimate of long-term causal effect CE(T) in Eq. (1). |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper uses "experimental data from Rapoport and Boebel [18], as reported by Mc Kelvey and Palfrey [15]". While these are cited papers, no direct access information (link, DOI, specific repository) for the dataset itself is provided, nor is it explicitly stated to be a well-known public dataset with clear access. |
| Dataset Splits | Yes | To evaluate our method, we consider the last period as long-term, and hold out data from this period. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | We choose diffuse priors for our parameters, specifically, φ U(0, 10), ψ U( 5, 5), and λ U( 10, 10). Given φ we sample the initial behaviors as Dirichlet, i.e., β1(0; Z) Dir(φ) and β0(0; Z) Dir(φ), independently. As the temporal model, we adopt the lag-one vector autoregressive model, also known as VAR(1). For the behavioral model, we adopt the quantal p-response (QLp) model [20], which has been successful in predicting human actions in real-world experiments [22]. We choose p = 3 behaviors, namely B = {b0, b1, b2} of increased sophistication parametrized by λ = (λ[1], λ[2], λ[3]) R3. |