Tobias Freidling
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    • Selective Randomization Inference for Adaptive Experiments
    • Optimization-based Sensitivity Analysis for Unmeasured Confounding using Partial Correlations
    • Post-selection Inference with HSIC-Lasso
    • Confidence in Causal Discovery with Linear Causal Models
    • Model Uncertainty in Statistical Inference
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    • Selective Randomization Inference for Adaptive Experiments
    • Selective Randomization Inference for Adaptive Experiments
    • Selective Randomization Inference for Adaptive Experiments
    • Selective Randomization Inference for Adaptive Studies
    • Selective Randomization Inference for Adaptive Experiments
    • Selective Randomization Inference for Adaptive Clinical Studies
    • Selective Randomization Inference for Adaptive Studies
    • Sensitivity Analysis with the R2-Calculus
    • Sensitivity Analysis with the R2-Calculus
    • Sensitivity Analysis with the R2-Calculus
    • Sensitivity Analysis with the R2-Calculus
    • Sensitivity Analysis with the R2-Calculus
    • Post-selection inference with HSIC-Lasso
    • Post-selection inference with HSIC-Lasso
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Pandas

2023-10-26 · 1 min read
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Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures.

Last updated on 2023-10-26
Hugo Wowchemy Markdown
Tobias Freidling
Authors
Tobias Freidling
PhD student in Statistics
PyTorch 2023-10-26 →

© 2024 Tobias Freidling. This work is licensed under CC BY NC ND 4.0

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