Long-term Causal Effects via Behavioral Game Theory
Authors: Panagiotis Toulis, David C. Parkes
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 panos.toulis@chicagobooth.edu David C. Parkes Department of Computer Science Harvard University Cambridge, MA, 02138 parkes@eecs.harvard.edu |
| 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. |