June  2020, 2(2): 155-172. doi: 10.3934/fods.2020009

A Bayesian nonparametric test for conditional independence

Department of Mathematics, Imperial College London, UK

Published  July 2020

Fund Project: Supported by EPSRC grant EP/R013519/1

This article introduces a Bayesian nonparametric method for quantifying the relative evidence in a dataset in favour of the dependence or independence of two variables conditional on a third. The approach uses Pólya tree priors on spaces of conditional probability densities, accounting for uncertainty in the form of the underlying distributions in a nonparametric way. The Bayesian perspective provides an inherently symmetric probability measure of conditional dependence or independence, a feature particularly advantageous in causal discovery and not employed in existing procedures of this type.

Citation: Onur Teymur, Sarah Filippi. A Bayesian nonparametric test for conditional independence. Foundations of Data Science, 2020, 2 (2) : 155-172. doi: 10.3934/fods.2020009
References:
[1]

J. O. Berger and A. Guglielmi, Bayesian and conditional frequentist testing of a parametric model versus nonparametric alternatives, J. Amer. Statist. Assoc., 96 (2001), 174-184.  doi: 10.1198/016214501750333045.  Google Scholar

[2]

W. Bergsma, Testing conditional independence for continuous random variables, Report Eurandom, 2004. Google Scholar

[3]

T. B. BerrettY. WangR. F. Barber and R. J. Samworth, The conditional permutation test for independence while controlling for confounders, J. R. Stat. Soc. B, 82 (2020), 175-197.  doi: 10.1111/rssb.12340.  Google Scholar

[4]

E. CandèsY. FanL. Janson and J. Lv, Panning for gold: Model-X knockoffs for high dimensional controlled variable selection, J. R. Stat. Soc. Ser. B. Stat. Methodol., 80 (2018), 551-577.  doi: 10.1111/rssb.12265.  Google Scholar

[5]

G. Doran, K. Muandet, K. Zhang and B. Schölkopf, A permutation-based kernel conditional independence test, Proc. 30th Conf. UAI, 132–141. Google Scholar

[6]

M. Escobar and M. West, Bayesian density estimation and inference using mixtures, J. Amer. Statist. Assoc., 90 (1995), 577-588.  doi: 10.1080/01621459.1995.10476550.  Google Scholar

[7]

S. Filippi and C. Holmes, A Bayesian nonparametric approach to testing for dependence between random variables, Bayesian Anal., 12 (2017), 919-938.  doi: 10.1214/16-BA1027.  Google Scholar

[8]

R. Fisher, The distribution of the partial correlation coefficient, Metron, 3 (1924), 329-332.   Google Scholar

[9]

K. Fukumizu, A. Gretton, X. Sun and B. Schölkopf, Kernel measures of conditional dependence, Adv. Neural Inf. Process. Syst., 20, 489–496. Google Scholar

[10]

S. Ghosal and A. van der Vaart, Fundamentals of Nonparametric Bayesian Inference, Cambridge Series in Statistical and Probabilistic Mathematics, 44. Cambridge University Press, Cambridge, 2017. doi: 10.1017/9781139029834.  Google Scholar

[11]

J. K. Ghosh and R. V. Ramamoorthi, Bayesian Nonparametrics, Springer-Verlag, New York, 2003.  Google Scholar

[12]

P. Giudici, Bayes factors for zero partial covariances, J. Statist. Plann. Inference, 46 (1995), 161-174.  doi: 10.1016/0378-3758(94)00101-Z.  Google Scholar

[13]

T. E. Hanson, Inference for mixtures of finite Pólya tree models, J. Amer. Statist. Assoc., 101 (2006), 1548-1565.  doi: 10.1198/016214506000000384.  Google Scholar

[14]

T. Hanson and W. O. Johnson, Modeling regression error with a mixture of Pólya trees, J. Amer. Statist. Assoc., 97 (2002), 1020-1033.  doi: 10.1198/016214502388618843.  Google Scholar

[15]

N. Harris and M. Drton, PCalgorithm for nonparanormal graphical models, J. Mach. Learn. Res., 14 (2013), 3365-3383.   Google Scholar

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P. Hoyer, D. Janzing, J. Mooij, J. Peters and B. Schölkopf, Nonlinear causal discovery with additive noise models, Adv. Neural Inf. Process. Syst. 21, 689–696. Google Scholar

[17]

T.-M. Huang, Testing conditional independence using maximal nonlinear conditional correlation, Ann. Statist., 38 (2010), 2047-2091.  doi: 10.1214/09-AOS770.  Google Scholar

[18]

R. E. Kass and A. E. Raftery, Bayes factors, J. Amer. Statist. Assoc., 90 (1995), 773-795.  doi: 10.1080/01621459.1995.10476572.  Google Scholar

[19]

T. Kunihama and D. B. Dunson, Nonparametric Bayes inference on conditional independence, Biometrika, 103 (2016), 35-47.  doi: 10.1093/biomet/asv060.  Google Scholar

[20]

M. Lavine, Some aspects of Pólya tree distributions for statistical modelling, Ann. Statist., 20 (1992), 1222-1235.  doi: 10.1214/aos/1176348767.  Google Scholar

[21]

M. Lavine, More aspects of Pólya tree distributions for statistical modelling, Ann. Statist., 22 (1994), 1161-1176.  doi: 10.1214/aos/1176325623.  Google Scholar

[22]

L. Ma, Adaptive testing of conditional association through recursive mixture modeling, J. Amer. Statist. Assoc., 108 (2013), 1493-1505.  doi: 10.1080/01621459.2013.838899.  Google Scholar

[23]

L. Ma, Recursive partitioning and multi-scale modeling on conditional densities, Electron. J. Stat., 11 (2017), 1297-1325.  doi: 10.1214/17-EJS1254.  Google Scholar

[24] D. J. C. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003.   Google Scholar
[25]

D. Margaritis, Distribution-free learning of bayesian network structure in continuous domains, Proc. 20th Nat. Conf. Artificial Intel., (2005), 825–830. Google Scholar

[26]

R. D. MauldinW. D. Sudderth and S. C. Williams, Pólya trees and random distributions, Ann. Statist., 20 (1992), 1203-1221.  doi: 10.1214/aos/1176348766.  Google Scholar

[27]

S. M. Paddock, Randomized Pólya Trees: Bayesian Nonparametrics for Multivariate Data Analysis, Thesis (Ph.D.)–Duke University. 1999.  Google Scholar

[28] J. Pearl, Causality: Models, Reasoning, and Inference, Cambridge University Press, 2009.  doi: 10.1017/CBO9780511803161.  Google Scholar
[29] J. PetersD. Janzing and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms, MIT Press, Cambridge, MA, 2017.   Google Scholar
[30]

J. PetersJ. MooijD. Janzing and B. Schölkopf, Causal discovery with continuous additive noise models, J. Mach. Learn. Res., 15 (2014), 2009-2053.   Google Scholar

[31]

J. Ramsey, A scalable conditional independence test for nonlinear, non-Gaussian data, arXiv: 1401.5031. Google Scholar

[32]

J. Runge, Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information, arXiv: 1709.01447. Google Scholar

[33]

F. Saad and V. Mansinghka, Detecting dependencies in sparse, multivariate databases using probabilistic programming and non-parametric Bayes, Proc. Mach. Learn. Res., 46 (2017), 632-641.   Google Scholar

[34]

R. Shah and J. Peters, The hardness of conditional independence testing and the generalised covariance measure, arXiv: 1804.07203. Google Scholar

[35]

P. Spirtes and C. Glymour, An algorithm for fast recovery of sparse causal graphs, Soc. Sci. Comput. Rev., 9 (1991), 62-72.  doi: 10.1177/089443939100900106.  Google Scholar

[36]

E. Strobl, K. Zhang and S. Visweswaran, Approximate kernel-based conditional independence tests for fast non-parametric causal discovery, J. Causal Inference, (2019), 20180017. doi: 10.1515/jci-2018-0017.  Google Scholar

[37]

L. Su and H. White, A consistent characteristic function-based test for conditional independence, J. Econom., 141 (2007), 807-834.  doi: 10.1016/j.jeconom.2006.11.006.  Google Scholar

[38]

L. Su and H. White, A nonparametric Hellinger metric test for conditional independence, Econom. Theory, 24 (2008), 829-864.  doi: 10.1017/S0266466608080341.  Google Scholar

[39]

W. H. Wong and L. Ma, Optional Pólya tree and Bayesian inference, Ann. Statist., 38 (2010), 1433-1459.  doi: 10.1214/09-AOS755.  Google Scholar

[40]

Q. Zhang, S. Filippi, S. Flaxman and D. Sejdinovic, Feature-to-feature regression for a two-step conditional independence test, Proc. 33rd Conf. UAI, 2017. Google Scholar

[41]

K. Zhang, J. Peters, D. Janzing and B. Schölkopf, Kernel-based conditional independence test and application in causal discovery, arXiv: 1202.3775. Google Scholar

[42]

J. ZhangL. Yang and X. Wu, Pólya tree priors and their estimation with multi-group data, Stat. Pap., 60 (2019), 499-525.  doi: 10.1007/s00362-016-0852-x.  Google Scholar

show all references

References:
[1]

J. O. Berger and A. Guglielmi, Bayesian and conditional frequentist testing of a parametric model versus nonparametric alternatives, J. Amer. Statist. Assoc., 96 (2001), 174-184.  doi: 10.1198/016214501750333045.  Google Scholar

[2]

W. Bergsma, Testing conditional independence for continuous random variables, Report Eurandom, 2004. Google Scholar

[3]

T. B. BerrettY. WangR. F. Barber and R. J. Samworth, The conditional permutation test for independence while controlling for confounders, J. R. Stat. Soc. B, 82 (2020), 175-197.  doi: 10.1111/rssb.12340.  Google Scholar

[4]

E. CandèsY. FanL. Janson and J. Lv, Panning for gold: Model-X knockoffs for high dimensional controlled variable selection, J. R. Stat. Soc. Ser. B. Stat. Methodol., 80 (2018), 551-577.  doi: 10.1111/rssb.12265.  Google Scholar

[5]

G. Doran, K. Muandet, K. Zhang and B. Schölkopf, A permutation-based kernel conditional independence test, Proc. 30th Conf. UAI, 132–141. Google Scholar

[6]

M. Escobar and M. West, Bayesian density estimation and inference using mixtures, J. Amer. Statist. Assoc., 90 (1995), 577-588.  doi: 10.1080/01621459.1995.10476550.  Google Scholar

[7]

S. Filippi and C. Holmes, A Bayesian nonparametric approach to testing for dependence between random variables, Bayesian Anal., 12 (2017), 919-938.  doi: 10.1214/16-BA1027.  Google Scholar

[8]

R. Fisher, The distribution of the partial correlation coefficient, Metron, 3 (1924), 329-332.   Google Scholar

[9]

K. Fukumizu, A. Gretton, X. Sun and B. Schölkopf, Kernel measures of conditional dependence, Adv. Neural Inf. Process. Syst., 20, 489–496. Google Scholar

[10]

S. Ghosal and A. van der Vaart, Fundamentals of Nonparametric Bayesian Inference, Cambridge Series in Statistical and Probabilistic Mathematics, 44. Cambridge University Press, Cambridge, 2017. doi: 10.1017/9781139029834.  Google Scholar

[11]

J. K. Ghosh and R. V. Ramamoorthi, Bayesian Nonparametrics, Springer-Verlag, New York, 2003.  Google Scholar

[12]

P. Giudici, Bayes factors for zero partial covariances, J. Statist. Plann. Inference, 46 (1995), 161-174.  doi: 10.1016/0378-3758(94)00101-Z.  Google Scholar

[13]

T. E. Hanson, Inference for mixtures of finite Pólya tree models, J. Amer. Statist. Assoc., 101 (2006), 1548-1565.  doi: 10.1198/016214506000000384.  Google Scholar

[14]

T. Hanson and W. O. Johnson, Modeling regression error with a mixture of Pólya trees, J. Amer. Statist. Assoc., 97 (2002), 1020-1033.  doi: 10.1198/016214502388618843.  Google Scholar

[15]

N. Harris and M. Drton, PCalgorithm for nonparanormal graphical models, J. Mach. Learn. Res., 14 (2013), 3365-3383.   Google Scholar

[16]

P. Hoyer, D. Janzing, J. Mooij, J. Peters and B. Schölkopf, Nonlinear causal discovery with additive noise models, Adv. Neural Inf. Process. Syst. 21, 689–696. Google Scholar

[17]

T.-M. Huang, Testing conditional independence using maximal nonlinear conditional correlation, Ann. Statist., 38 (2010), 2047-2091.  doi: 10.1214/09-AOS770.  Google Scholar

[18]

R. E. Kass and A. E. Raftery, Bayes factors, J. Amer. Statist. Assoc., 90 (1995), 773-795.  doi: 10.1080/01621459.1995.10476572.  Google Scholar

[19]

T. Kunihama and D. B. Dunson, Nonparametric Bayes inference on conditional independence, Biometrika, 103 (2016), 35-47.  doi: 10.1093/biomet/asv060.  Google Scholar

[20]

M. Lavine, Some aspects of Pólya tree distributions for statistical modelling, Ann. Statist., 20 (1992), 1222-1235.  doi: 10.1214/aos/1176348767.  Google Scholar

[21]

M. Lavine, More aspects of Pólya tree distributions for statistical modelling, Ann. Statist., 22 (1994), 1161-1176.  doi: 10.1214/aos/1176325623.  Google Scholar

[22]

L. Ma, Adaptive testing of conditional association through recursive mixture modeling, J. Amer. Statist. Assoc., 108 (2013), 1493-1505.  doi: 10.1080/01621459.2013.838899.  Google Scholar

[23]

L. Ma, Recursive partitioning and multi-scale modeling on conditional densities, Electron. J. Stat., 11 (2017), 1297-1325.  doi: 10.1214/17-EJS1254.  Google Scholar

[24] D. J. C. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003.   Google Scholar
[25]

D. Margaritis, Distribution-free learning of bayesian network structure in continuous domains, Proc. 20th Nat. Conf. Artificial Intel., (2005), 825–830. Google Scholar

[26]

R. D. MauldinW. D. Sudderth and S. C. Williams, Pólya trees and random distributions, Ann. Statist., 20 (1992), 1203-1221.  doi: 10.1214/aos/1176348766.  Google Scholar

[27]

S. M. Paddock, Randomized Pólya Trees: Bayesian Nonparametrics for Multivariate Data Analysis, Thesis (Ph.D.)–Duke University. 1999.  Google Scholar

[28] J. Pearl, Causality: Models, Reasoning, and Inference, Cambridge University Press, 2009.  doi: 10.1017/CBO9780511803161.  Google Scholar
[29] J. PetersD. Janzing and B. Schölkopf, Elements of Causal Inference: Foundations and Learning Algorithms, MIT Press, Cambridge, MA, 2017.   Google Scholar
[30]

J. PetersJ. MooijD. Janzing and B. Schölkopf, Causal discovery with continuous additive noise models, J. Mach. Learn. Res., 15 (2014), 2009-2053.   Google Scholar

[31]

J. Ramsey, A scalable conditional independence test for nonlinear, non-Gaussian data, arXiv: 1401.5031. Google Scholar

[32]

J. Runge, Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information, arXiv: 1709.01447. Google Scholar

[33]

F. Saad and V. Mansinghka, Detecting dependencies in sparse, multivariate databases using probabilistic programming and non-parametric Bayes, Proc. Mach. Learn. Res., 46 (2017), 632-641.   Google Scholar

[34]

R. Shah and J. Peters, The hardness of conditional independence testing and the generalised covariance measure, arXiv: 1804.07203. Google Scholar

[35]

P. Spirtes and C. Glymour, An algorithm for fast recovery of sparse causal graphs, Soc. Sci. Comput. Rev., 9 (1991), 62-72.  doi: 10.1177/089443939100900106.  Google Scholar

[36]

E. Strobl, K. Zhang and S. Visweswaran, Approximate kernel-based conditional independence tests for fast non-parametric causal discovery, J. Causal Inference, (2019), 20180017. doi: 10.1515/jci-2018-0017.  Google Scholar

[37]

L. Su and H. White, A consistent characteristic function-based test for conditional independence, J. Econom., 141 (2007), 807-834.  doi: 10.1016/j.jeconom.2006.11.006.  Google Scholar

[38]

L. Su and H. White, A nonparametric Hellinger metric test for conditional independence, Econom. Theory, 24 (2008), 829-864.  doi: 10.1017/S0266466608080341.  Google Scholar

[39]

W. H. Wong and L. Ma, Optional Pólya tree and Bayesian inference, Ann. Statist., 38 (2010), 1433-1459.  doi: 10.1214/09-AOS755.  Google Scholar

[40]

Q. Zhang, S. Filippi, S. Flaxman and D. Sejdinovic, Feature-to-feature regression for a two-step conditional independence test, Proc. 33rd Conf. UAI, 2017. Google Scholar

[41]

K. Zhang, J. Peters, D. Janzing and B. Schölkopf, Kernel-based conditional independence test and application in causal discovery, arXiv: 1202.3775. Google Scholar

[42]

J. ZhangL. Yang and X. Wu, Pólya tree priors and their estimation with multi-group data, Stat. Pap., 60 (2019), 499-525.  doi: 10.1007/s00362-016-0852-x.  Google Scholar

Figure 1.  Construction of a Pólya tree distribution on $ \Omega = [0,1] $. From each set $ C_\ast $, a particle of probability mass passes to the left with (random) probability $ \theta_{\ast0} $ and to the right with probability $ \theta_{\ast1} = 1-\theta_{\ast0} $, with all $ \theta_\ast $ being independently Beta-distributed as described in the main text
Figure 2.  Pseudocode for the proposed Bayesian nonparametric test for conditional independence
Figure 3.  Application of the proposed Bayesian testing procedure to four synthetic datasets supported on $ [0,1]^3 $, chosen such that all combinations of unconditional and conditional dependence/independence are represented. The final column gives the ensemble of probabilities of conditional dependence $ p(H_1|W) $ output by the test over 100 repetitions at varying values of data size $ N $, with the blue line representing the median, and the dark and light shaded regions representing the (25, 75)-percentile and (5, 95)-percentile ranges respectively
Figure 4.  Marginal scatter plots from the CalCOFI Bottle dataset showing the pairwise relationships between $\texttt{Salnty}$, $\texttt{Oxy_µmol.Kg}$ and $\texttt{T_degC}$. The nonlinear nature of the dependences is immediately apparent
Figure 5.  Example pairwise dependence graphs output by the Bayesian conditional independence test for five variables from the CalCOFI dataset, conditional on $\texttt{T_degC}$, for four different sizes of subsample drawn from the complete dataset. The numbers associated with each edge are the posterior probabilities of conditional dependence $ p(H_1|W^{(N)}) $ and are given to two decimal places; where no edge is shown, this indicates $ p(H_1|W^{(N)})<0.005 $
Figure 6.  Box-plots giving the output posterior probability of conditional dependence $ p(H_1|W^{(N)}) $ for 100 repetitions of the Bayesian conditional independence test applied to randomly-drawn subsamples of various sizes $ N $ from the CalCOFI dataset. The left-hand plot gives a representative example of a pair of variables conditionally dependent given $\texttt{T_degC}$, while the right-hand plot gives a representative conditionally independent pair
Figure 7.  Top left: Heat map of conditional marginal likelihood values for the three constituent models over $ \Omega_X $, $ \Omega_Y $ and $ \Omega_XY $ for the second and third models of Figure 3. Top right: 'Slices' from this heatmap with $ \rho = 0.5 $. Bottom: Test outputs for 100 repetitions of the second and third models of Figure 3. Red plots fix $ c = 1 $ (output identical to Figure 3), while the blue plots use the optimising values $ \hat{c} $ from the plot above
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