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We present DoWhy-GCM, an extension of the DoWhy Python library, which leverages graphical causal models.
Causal discovery toolbox: Uncover causal relationships in Python, 2019
D. Kalainathan and O. Goudet · 1903
Earlier work this paper cites.
Estimating causal effects of treatments in randomized and nonrandomized studies
D. B. Rubin · 1974
Earlier work this paper cites.
CausalML: Python package for causal machine learning, 2020
H. Chen, T. Harinen, J. Lee, M. Yung, and Z. Zhao · 2002
Earlier work this paper cites.
Exploring network structure, dynamics, and function using networkx
A. A. Hagberg, D. A. Schult, and P. J. Swart · 2008
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
P. Hoyer, D. Janzing, J. Mooij, J. Peters, and B Schölkopf · 2009
Earlier work this paper cites.
Causality: Models, Reasoning, and Inference
J. Pearl · 2009
Earlier work this paper cites.
On the identifiability of the post-nonlinear causal model
K. Zhang and A. Hyvärinen · 2009
Earlier work this paper cites.
Data Structures for Statistical Computing in Python
W. McKinney · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Cited alongside, same era.
DoWhy: An end-to-end library for causal inference, 2020
A. Sharma and E. Kiciman · 2011
Cited alongside, same era.
Quantifying causal influences
D. Janzing, D. Balduzzi, M. Grosse-Wentrup, and B. Schölkopf · 2013
Cited alongside, same era.
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
G. W. Imbens and D. B. Rubin · 2015
Cited alongside, same era.
A unified approach to interpreting model predictions
S. Lundberg and S. Lee · 2017
Cited alongside, same era.
Elements of Causal Inference – Foundations and Learning Algorithms
Feature relevance quantification in explainable AI: A causal problem
D. Janzing, L. Minorics, and P. Blöbaum · 2020
Later among the works it cites.
WhyNot, 2020
J. Miller, C. Hsu, J. Troutman, J. Perdomo, T. Zrnic, L. Liu, Y. Sun, L. Schmidt, and M. Hardt · 2020
Later among the works it cites.
Explaining machine learning classifiers through diverse counterfactual explanations
R. K Mothilal, A. Sharma, and C. Tan · 2020
Later among the works it cites.
SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C J Carey, İ. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors · 2020
Later among the works it cites.
CausalNex, 10 2021
P. Beaumont, B. Horsburgh, P. Pilgerstorfer, A. Droth, R. Oentaryo, S. Ler, H. Nguyen, G. A Ferreira, Z. Patel, and W. Leong · 2021
Later among the works it cites.
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J. Peters, D. Janzing, and B. Schölkopf · 2017
Cited alongside, same era.
EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation
K. Battocchi, E. Dillon, M. Hei, G. Lewis, P. Oka, M. Oprescu, and V. Syrgkanis · 2019
Cited alongside, same era.
Array programming with NumPy
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. Fernández del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant · 2020
Cited alongside, same era.
Explaining the root causes of unit-level changes, 2022a
K. Budhathoki, G. Michailidis, and D. Janzing
Cited in the paper.
Causal structure-based root cause analysis of outliers
K. Budhathoki, L. Minorics, P. Blöbaum, and D. Janzing
Cited in the paper.
Tetrad—a toolbox for causal discovery
J. D Ramsey, K. Zhang, M Glymour, R. S. Romero, B. Huang, I. Ebert-Uphoff, S. Samarasinghe, E. A Barnes, and C. Glymour
Cited in the paper.
Why did the distribution change?
K. Budhathoki, D. Janzing, P. Bloebaum, and H. Ng · 2021
Later among the works it cites.
Toward falsifying causal graphs using a permutation-based test, 2023
E. Eulig, A. A. Mastakouri, P. Blöbaum, M. Hardt, and D. Janzing · 2023
Closest in time.
Quantifying intrinsic causal contributions via structure preserving interventions
D. Janzing, P. Blöbaum, A. A Mastakouri, P. M Faller, L. Minorics, and K. Budhathoki · 2024
Closest in time.