Causal Inference out of Control: Estimating Performativity without Treatment Randomization
Authors: Gary Cheng, Moritz Hardt, Celestine Mendler-Dünner
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our claims with an analysis of ready-to-use finite sample estimators and empirical investigations. More broadly, our results deriving identifiability conditions tailored to digital platform settings illustrate a fruitful interplay of control theory and causal inference. (...) To complement our study, we propose a two-stage regression estimator and an adjustment formula estimator for estimating PEt from finite samples with theoretical guarantees. We also simulate a recommendation system to empirically test the efficacy of our assumptions at reducing overlap violations. We present more experiments in the Appendix, including ones using real microeconomic data. |
| Researcher Affiliation | Academia | 1Stanford University Department of Electrical Engineering 2Max Planck Institute for Intelligent Systems, T ubingen, and T ubingen AI Center 3ELLIS Institute T ubingen. Correspondence to: Gary Cheng <chenggar@stanford.edu>. |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm'. |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing the code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | Yes | We simulate an online platform with items, recommendations, users, and ratings using Rec Lab (Krauth et al., 2020) (...) We use an avocado time series dataset (Kiggins, 2018) |
| Dataset Splits | No | The paper does not specify explicit training, validation, or test dataset splits. It mentions using 'semi-synthetic recommender system data' and an 'avocado time series dataset' but does not detail how these datasets were partitioned for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU models, CPU specifications, or cloud computing instance types. |
| Software Dependencies | Yes | For RF-DML, the residualizing procedure uses a random forest model implemented using sci-kit-learn 1.2.0. |
| Experiment Setup | Yes | We initiate the EASE recommender by simulating 100 cycles. In the no-update variation, we fix the EASE recommender weights to their initial value, and in the other variation, we continue to update the weights during the following N cycles based on user ratings. (...) We discretize the logged price into two buckets: ( 0.479,0.131],(0.131,0.683] and the logged demand into two buckets (14.539,15.014],(15.014,15.837]. |