https://github.com/AliciaCurth/IF-learn
공식 코드Estimating Structural Target Functions using Machine Learning and Influence Functions
Papers with Code 아카이브
Econometrics
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DataScienceForPublicPolicy/hypML
공식 코드Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?
https://github.com/Ganna85/TV-Entropy
공식 코드https://github.com/Hesterhuijsdens/GWP-SMC
공식 코드Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo
https://github.com/SlavovLab/STLS
공식 코드https://github.com/Yifanli1103/Geospatial-Map---KNIME
공식 코드https://github.com/amazon-research/quantile-aggregation
공식 코드https://github.com/antti-pollanen/ci_noisy_treatment
공식 코드Identifiable causal inference with noisy treatment and no side information
https://github.com/apoorvalal/TestingInEventStudies
공식 코드When can we get away with using the two-way fixed effects regression?
https://github.com/caioflp/sagd-iv
공식 코드Nonparametric Instrumental Variable Regression through Stochastic Approximate Gradients
https://github.com/geighei/gxe_4practitioners
공식 코드The Economics and Econometrics of Gene-Environment Interplay
https://github.com/gkswamy98/causal_il
공식 코드https://github.com/ittai-rubinstein/ols_robustness
공식 코드Robustness Auditing for Linear Regression: To Singularity and Beyond
https://github.com/jeremyhengjm/diffusionbridge
공식 코드https://github.com/jkirkby3/fypy
공식 코드Calibration and Option Pricing with Stochastic Volatility and Double Exponential Jumps
https://github.com/marti5ini/ci-over-time
공식 코드https://github.com/nicolagnecco/bcf-numerical-experiments
공식 코드Boosted Control Functions: Distribution generalization and invariance in confounded models
https://github.com/nixay/causal-stonet
공식 코드Causal-StoNet: Causal Inference for High-Dimensional Complex Data
https://github.com/oknakfm/npir
공식 코드Minimax Analysis for Inverse Risk in Nonparametric Planer Invertible Regression
https://github.com/pietrolesci/memorisation-profiles
공식 코드https://github.com/quantlet/mlvsgarch
공식 코드Tail-risk protection: Machine Learning meets modern Econometrics
https://github.com/rrsguim/phd_economics
공식 코드https://github.com/schmalwb/topic_model_time_series
공식 코드Quantitative Tools for Time Series Analysis in Natural Language Processing: A Practitioners Guide
https://github.com/sorawitj/hsic-x
공식 코드Exploiting Independent Instruments: Identification and Distribution Generalization
https://github.com/stathastic/tailefficienttest
공식 코드The Efficient Tail Hypothesis: An Extreme Value Perspective on Market Efficiency
https://github.com/stevenacampbell/Macroscopic-Properties-of-Equity-Markets
공식 코드Macroscopic properties of equity markets: stylized facts and portfolio performance
https://github.com/vbayeslab/deepvol
공식 코드Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting
https://github.com/vbayeslab/realrech
공식 코드https://github.com/xymmmm00/diffusion_factor_model
공식 코드Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure
https://github.com/yukiyuqichen/GeoBlockchain
공식 코드https://github.com/allenai/dolma
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/bramiozo/PubScience
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/dhavala/bqr
Estimation and Applications of Quantiles in Deep Binary Classification
https://github.com/lhagiimn/A-Locally-Adaptive-Regression
https://github.com/lx10077/AveQLearning
Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling
https://github.com/nasiryahm/STDWI
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.