Discovering Causal Models with Optimization: Confounders, Cycles, and Instrument Validity

Published Online:https://doi.org/10.1287/mnsc.2021.02066

References

  • Angrist JD, Krueger AB (1991) Does compulsory school attendance affect schooling and earnings? Quart. J. Econom. 106(4):979–1014.CrossrefGoogle Scholar
  • Angrist JD, Krueger AB (2001) Instrumental variables and the search for identification: From supply and demand to natural experiments. J. Econom. Perspect. 15(4):69–85.CrossrefGoogle Scholar
  • Angrist JD, Imbens GW, Rubin DB (1996) Identification of causal effects using instrumental variables. J. Amer. Statist. Assoc. 91(434):444–455.CrossrefGoogle Scholar
  • Balke A, Pearl J (1997) Bounds on treatment effects from studies with imperfect compliance. J. Amer. Statist. Assoc. 92(439):1171–1176.CrossrefGoogle Scholar
  • Barnhart C, Johnson EL, Nemhauser GL, Savelsbergh MWP, Vance PH (1998) Branch-and-price: Column generation for solving huge integer programs. Oper. Res. 46(3):316–329.LinkGoogle Scholar
  • Bartlett M, Cussens J (2017) Integer linear programming for the Bayesian network structure learning problem. Artificial Intelligence 244:258–271.CrossrefGoogle Scholar
  • Belloni A, Chernozhukov V, Hansen C (2014) Inference on treatment effects after selection among high-dimensional controls. Rev. Econom. Stud. 81(2):608–650.CrossrefGoogle Scholar
  • Bound J, Jaeger DA, Baker RM (1995) Problems with instrumental variables estimation when the correlation between the instruments and the endogenous explanatory variable is weak. J. Amer. Statist. Assoc. 90(430):443–450.Google Scholar
  • Bowden RJ, Turkington DA (1990) Instrumental Variables, vol. 8 (Cambridge University Press, Cambridge, UK).Google Scholar
  • Buckles KS, Hungerman DM (2013) Season of birth and later outcomes: Old questions, new answers. Rev. Econom. Statist. 95(3):711–724.CrossrefGoogle Scholar
  • Card D (1993) Using geographic variation in college proximity to estimate the return to schooling. NBER Working Paper No. 4483, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Card D (1999) The causal effect of education on earnings. Handbook Labor Econom. 3:1801–1863.CrossrefGoogle Scholar
  • Chen R, Dash S, Gao T (2021) Integer programming for causal structure learning in the presence of latent variables. Internat. Conf. Machine Learn. (PMLR, New York), 1550–1560.Google Scholar
  • Colombo D, Maathuis MH, Kalisch M, Richardson TS (2012) Learning high-dimensional directed acyclic graphs with latent and selection variables. Ann. Statist. 40(1):294–321.CrossrefGoogle Scholar
  • Cussens J (2011) Bayesian network learning with cutting planes. Proc. Twenty-Eighth Conf. Uncertainty Artificial Intelligence (AUAI Press, Arlington, VA), 153–160.Google Scholar
  • Eberhardt F (2017) Introduction to the foundations of causal discovery. Internat. J. Data Sci. Anal. 3(2):81–91.CrossrefGoogle Scholar
  • Efron B, Tibshirani RJ (1994) An Introduction to the Bootstrap (Chapman and Hall/CRC, Norwell, MA).CrossrefGoogle Scholar
  • Evans RJ (2016) Graphs for margins of Bayesian networks. Scand. J. Statist. 43(3):625–648.CrossrefGoogle Scholar
  • Forré P, Mooij JM (2018) Constraint-based causal discovery for non-linear structural causal models with cycles and latent confounders. Preprint, submitted July 9, https://arxiv.org/abs/1807.03024.Google Scholar
  • Geiger D, Pearl J (1988) On the Logic of Influence Diagrams (University of California (Los Angeles), Computer Science Department, Los Angeles).Google Scholar
  • Geiger D, Verma T, Pearl J (1990) Identifying independence in Bayesian networks. Networks 20(5):507–534.CrossrefGoogle Scholar
  • Heckman JJ, Vytlacil E (2005) Structural equations, treatment effects, and econometric policy evaluation. Econometrica 73(3):669–738.CrossrefGoogle Scholar
  • Hyttinen A, Eberhardt F, Hoyer PO (2012) Learning linear cyclic causal models with latent variables. J. Machine Learn. Res. 13(1):3387–3439.Google Scholar
  • Hyttinen A, Eberhardt F, Järvisalo M (2014) Constraint-based causal discovery: Conflict resolution with answer set programming. Proc. Thirtieth Conf. Uncertainty Artificial Intelligence (AUAI Press, Corvallis, OR), 340–349.Google Scholar
  • Hyttinen A, Saikko P, Järvisalo M (2017) A core-guided approach to learning optimal causal graphs. Proc. 26th Internat. Joint Conf. Artificial Intelligence (IJCAI 2017) (AAAI Press, Palo Alto, CA).Google Scholar
  • Hyttinen A, Hoyer PO, Eberhardt F, Järvisalo M (2013) Discovering cyclic causal models with latent variables: A general SAT-based procedure. Proc. Twenty-Ninth Conf. Uncertainty Artificial Intelligence (AUAI Press, Corvallis, OR), 301–310.Google Scholar
  • Imbens GW, Rosenbaum PR (2005) Robust, accurate confidence intervals with a weak instrument: Quarter of birth and education. J. Royal Statist. Soc. Ser. A 168(1):109–126.CrossrefGoogle Scholar
  • Jaakkola T, Sontag D, Globerson A, Meila M (2010) Learning Bayesian network structure using LP relaxations. Proc. Thirteenth Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 358–365.Google Scholar
  • Kédagni D, Mourifié I (2020) Generalized instrumental inequalities: Testing the instrumental variable independence assumption. Biometrika 107(3):661–675.CrossrefGoogle Scholar
  • Kestenbaum B (1987) Seasonality of birth: Two findings from the decennial census. Soc. Biol. 34(3–4):244–248.Google Scholar
  • Kitagawa T (2015) A test for instrument validity. Econometrica 83(5):2043–2063.CrossrefGoogle Scholar
  • Kucukyavuz S, Shojaie A, Manzour H, Wei L, Wu H-H (2020) Consistent second-order conic integer programming for learning Bayesian networks. Preprint, submitted May 29, https://arxiv.org/abs/2005.14346.Google Scholar
  • Lubbecke ME, Desrosiers J (2005) Selected topics in column generation. Oper. Res. 53(6):1007–1023.LinkGoogle Scholar
  • Maathuis MH, Colombo D, Kalisch M, Bühlmann P (2010) Predicting causal effects in large-scale systems from observational data. Nat. Methods 7(4):247–248.CrossrefGoogle Scholar
  • Magliacane S, Claassen T, Mooij JM (2016) Ancestral causal inference. Adv. Neural Inform. Processing Systems 29:4466–4474.Google Scholar
  • Manzour H, Küçükyavuz S, Wu H-H, Shojaie A (2021) Integer programming for learning directed acyclic graphs from continuous data. Inform. J. Optim. 3(1):46–73.LinkGoogle Scholar
  • Meek C (1995) Strong completeness and faithfulness in Bayesian networks. Proc. Eleventh Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann Publishers, San Francisco), 411–418.Google Scholar
  • Mourifié I, Wan Y (2017) Testing local average treatment effect assumptions. Rev. Econom. Statist. 99(2):305–313.CrossrefGoogle Scholar
  • Murray MP (2006) Avoiding invalid instruments and coping with weak instruments. J. Econom. Perspect. 20(4):111–132.CrossrefGoogle Scholar
  • Park YW, Klabjan D (2017) Bayesian network learning via topological order. J. Machine Learn. Res. 18(1):3451–3482.Google Scholar
  • Pearl J (2000) Causality: Models, Reasoning and Inference (Cambridge University Press, Cambridge, UK).Google Scholar
  • Rantanen K, Hyttinen A, Järvisalo M (2020) Discovering causal graphs with cycles and latent confounders: An exact branch-and-bound approach. Internat. J. Approx. Reason. 117:29–49.CrossrefGoogle Scholar
  • Rantanen K, Hyttinen A, Järvisalo M (2018) Learning optimal causal graphs with exact search. Internat. Conf. Probabilistic Graphical Models (PMLR, New York), 344–355.Google Scholar
  • Richardson T (1996) Feedback models: Interpretation and discovery. PhD thesis, Carnegie Mellon, Pittsburgh.Google Scholar
  • Solus L, Wang Y, Uhler C (2021) Consistency guarantees for greedy permutation-based causal inference algorithms. Biometrika 108(4):795–814.CrossrefGoogle Scholar
  • Spirtes P (1995) Directed cyclic graphical representations of feedback models. Proc. Eleventh Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann Publishers, Inc., San Francisco), 491–498.Google Scholar
  • Spirtes P, Zhang K (2016) Causal discovery and inference: Concepts and recent methodological advances. Applied Informatics, vol. 3 (SpringerOpen, Cham, Switzerland), 1–28.Google Scholar
  • Spirtes P, Glymour CN, Scheines R, Heckerman D, Meek C, Cooper G, Richardson T (2000) Causation, Prediction, and Search (MIT Press, Cambridge, MA).Google Scholar
  • Staiger D, Stock JH (1997) Instrumental variables regression with weak instruments. Econometrica 65(3):557–586.CrossrefGoogle Scholar
  • Stock J (2002) Instrumental variables in economics and statistics. International Encyclopedia of the Social Sciences (Macmillan Reference USA, New York).Google Scholar
  • Stock JH, Wright JH, Yogo M (2002) A survey of weak instruments and weak identification in generalized method of moments. J. Bus. Econom. Statist. 20(4):518–529.CrossrefGoogle Scholar
  • Strobl EV, Spirtes PL, Visweswaran S (2019) Estimating and controlling the false discovery rate of the pc algorithm using edge-specific p-values. ACM Trans. Intell. Syst. Tech. 10(5):1–37.CrossrefGoogle Scholar
  • Teramoto R, Saito C, Shin-ichi F (2014) Estimating causal effects with a non-paranormal method for the design of efficient intervention experiments. BMC Bioinformatics 15(1):1–14.CrossrefGoogle Scholar
  • Triantafillou S, Tsamardinos I (2015) Constraint-based causal discovery from multiple interventions over overlapping variable sets. J. Machine Learn. Res. 16(1):2147–2205.Google Scholar
  • Uhler C, Raskutti G, Bühlmann P, Yu B (2013) Geometry of the faithfulness assumption in causal inference. Ann. Statist. 41(2):436–463.CrossrefGoogle Scholar
  • Verma T, Pearl J (1990) Equivalence and Synthesis of Causal Models (UCLA, Computer Science Department, Los Angeles).Google Scholar
  • Zhalama JZ, Eberhardt F, Mayer W (2017) SAT-based causal discovery under weaker assumptions. Proc. Thirty-Third Conf. Uncertainty Artificial Intelligence (AUAI Press, Corvallis, OR).Google Scholar
  • Zhang J, Spirtes P (2002) Strong faithfulness and uniform consistency in causal inference. Proc. Nineteenth Conf. Uncertainty Artificial Intelligence (Morgan Kaufmann Publishers, San Francisco), 632–639.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.