General Control Functions for Causal Effect Estimation from IVs
Authors: Aahlad Puli, Rajesh Ranganath
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate GCFN on low and high dimensional simulated data and on recovering the causal effect of slave export on modern community trust [30]. In section 4, we evaluate GCFN s causal effect estimation on simulated data with the outcome, treatment, and IV observed. |
| Researcher Affiliation | Academia | Aahlad Puli Computer Science New York University aahlad@nyu.edu Rajesh Ranganath Computer Science, Center for Data Science New York University rajeshr@cims.nyu.edu |
| Pseudocode | No | The paper does not contain any clearly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps in a code-like format. |
| Open Source Code | No | The paper does not contain any statements about releasing code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluate GCFN on simulated data... We then evaluate GCFN on high-dimensional data using simulations from Deep IV [18] and Deep GMM [7]... We also show recovery of the effect of slave export on current societal trust [30]. |
| Dataset Splits | Yes | All hyperparameters for VDE, except the mutual-information coefficient κ = λ/(1 + λ), and the outcome-stage were found by evaluating the respective objectives on a held-out validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam [22]' as an optimizer, but does not specify version numbers for any software, libraries, or frameworks used (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The encoder in VDE, fθ, is a 2-hidden-layer neural network fθ, which parametrizes a categorical likelihood qθ(ˆz = i | t = t, ϵ = ϵ). The decoder is also a 2-hidden-layer network... In all experiments, the hidden layers in both encoder and decoder networks have 100 units and use Re LU activations. The outcome model is also a 2-hidden-layer neural network with Re LU activations. For the simulated data, the hidden layers in the outcome model have 50 hidden units... we train on 5000 samples with a batch size of 500 for optimizing both VDE and the outcome model for 100 epochs with Adam [22]. |