Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
Authors: Raj Agrawal, Sam Witty, Andy Zane, Elias Bingham
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show empirically that estimators using MC-EIF are at parity with estimators using analytic EIFs. Finally, we present a novel capstone example using MC-EIF for optimal portfolio selection. |
| Researcher Affiliation | Collaboration | Raj Agrawal Basis Research Institute, Broad Institute raj@basis.ai Sam Witty Basis Research Institute, Broad Institute sam@basis.ai Andy Zane Basis Research Institute, UMass Amherst andy@basis.ai Eli Bingham Basis Research Institute, Broad Institute eli@basis.ai |
| Pseudocode | Yes | Algorithm 1 MC-EIF one step estimator |
| Open Source Code | Yes | Our MC-EIF implementation is publicly available in the Python package Chi Rho. All results shown here are end-to-end reproducible. |
| Open Datasets | Yes | All influence function computations are relative to an initial point estimate ˆϕ, found through maximum a posteriori estimation using 500 training datapoints. |
| Dataset Splits | Yes | Algorithm 1 MC-EIF one step estimator Input: Target functional ψ, initial estimate of parameters ˆϕ, held out datapoints {xn}N n=N/2+1, Number of Monte Carlo samples M |
| Hardware Specification | Yes | All experiments were run on an Apple M2 pro. In Figure 8, we plot the runtime of our method under various conditions. |
| Software Dependencies | No | The paper mentions software like 'pytorch' and 'Pyro' but does not specify their version numbers for reproducibility, which is required for a 'Yes' answer. |
| Experiment Setup | Yes | In Section 5, we consider the following model with confounders c, treatment t, and response y: µ0 N(0, 1), (intercept) , (outcome weights) , (propensity weights) τ N(0, 1), (treatment weight) cn N(0, ID), (confounders) tn | cn, π Bernoulli(logits = πT cn), (treatment assignment) yn N(τtn + ξT cn + µ0, 1), (response) |