Learning Deep Features in Instrumental Variable Regression
Authors: Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the empirical performance of DFIV in Section 4, covering three settings: a classical demand prediction example from econometrics, a challenging IV setting where the treatment consists of high-dimensional image data, and the problem of off-policy policy evaluation in reinforcement learning. |
| Researcher Affiliation | Collaboration | Liyuan Xu Gatsby Unit liyuan.jo.19@ucl.ac.uk Yutian Chen Deep Mind yutianc@google.com Siddarth Srinivasan University of Washington sidsrini@cs.washington.edu Nando de Freitas Deep Mind nandodefreitas@google.com Arnaud Doucet Deep Mind arnauddoucet@google.com Arthur Gretton Gatsby Unit arthur.gretton@gmail.com |
| Pseudocode | Yes | Algorithm 1 Deep Feature Instrumental Variable Regression |
| Open Source Code | Yes | The code is included in the supplemental material. |
| Open Datasets | Yes | We used the demand design dataset of Hartford et al. (2017) for benchmarking in the low and high-dimensional cases, and we propose a new setting for the high-dimensional case based on the d Sprites dataset (Matthey et al., 2017). In the deep RL context, we also apply DFIV to perform off-policy policy evaluation (OPE). The algorithms in the first two experiments are implemented using Py Torch (Paszke et al., 2019) and the OPE experiments are implemented using Tensor Flow (Abadi et al., 2015) and the Acme RL framework (Hoffman et al., 2020). The code is included in the supplemental material. |
| Dataset Splits | Yes | We tuned the regularizers λ1, λ2 as discussed in Appendix A, with the data evenly split for stage 1 and stage 2. |
| Hardware Specification | No | The algorithms in the first two experiments are implemented using Py Torch (Paszke et al., 2019) and the OPE experiments are implemented using Tensor Flow (Abadi et al., 2015) and the Acme RL framework (Hoffman et al., 2020). The code is included in the supplemental material. No specific hardware specifications (e.g., GPU/CPU models, memory) are mentioned for the experiments. |
| Software Dependencies | Yes | The algorithms in the first two experiments are implemented using Py Torch (Paszke et al., 2019) and the OPE experiments are implemented using Tensor Flow (Abadi et al., 2015) and the Acme RL framework (Hoffman et al., 2020). |
| Experiment Setup | Yes | Unless otherwise specified, all neural network-based algorithms are optimized using Adam with learning rate = 0.001, β1 = 0.9, β2 = 0.999 and ε = 10 8. For DFIV, we used the structure described in Table 4. The regularizer λ1, λ2 are both set to 0.1 as a result of the tuning procedure described in Appendix A. |