From Association to Causation via a Potential Outcomes Approach

Published Online:https://doi.org/10.1287/isre.1080.0184

References

  • Achen C. Let's put garbage-can regressions and garbage-can probits where they belong. Conflict Management Peace Sci. (2005) 22(4):327–339CrossrefGoogle Scholar
  • Ang S., Slaughter S. A., Ng K. Y. Human capital and institutional determinants of information technology compensation: Modeling multilevel and cross-level interactions. Management Sci. (2002) 48(11):1427–1445LinkGoogle Scholar
  • Angrist J. D., Krueger A. B., Ashenfelter O., Card D. Empirical strategies in labor economics. Handbook of Labor Economics (1999) (Elsevier, Amsterdam) 1277–1366CrossrefGoogle Scholar
  • Angrist J. D., Imbens G. W., Rubin D. B. Identification of causal effects using instrumental variables. J. Amer. Statist. Assoc. (1996) 91(434):444–455CrossrefGoogle Scholar
  • Aral S., Brynjolfsson E., Wu D. J. Which came first, IT or productivity? The virtuous cycle of investment & use in enterprise systems. Proc. 27th Internat. Conf. Inform. Systems (2006) AIS, Milwaukee, WI:1819–1840CrossrefGoogle Scholar
  • Banker R., Kauffman R., Morey R. Measuring gains in operational efficiency from information technology: A case study of the positran deployment at Hardee's Inc. J. Management Inform. Systems (1990) 7(2):29–54CrossrefGoogle Scholar
  • Barney J. B.Gaining and Sustaining Competitive Advantage (2002) 2nd ed.(Prentice Hall, Upper Saddle River, New Jersey) Google Scholar
  • Bharadwaj A. A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quart. (2000) 24(1):169–196CrossrefGoogle Scholar
  • Boulding W., Staelin R., Ehret M., Johnston W. J. A customer relationship management roadmap: What is known, potential pitfalls, and where to go. J. Marketing (2005) 69(4):155–166CrossrefGoogle Scholar
  • Bound J., Jaeger D. A., Baker R. M. Problems with instrumental variables estimation when the correlation between the instruments and endogenous explanatory variables is weak. J. Amer. Statist. Assoc. (1995) 90(430):443–450Google Scholar
  • Briggs D. C. Causal inference and the Heckman model. J. Educational Behavioral Statist. (2004) 29(4):397–420CrossrefGoogle Scholar
  • Campbell D. T. Reforms as experiments. Amer. Psych. (1969) 24(4):409–429CrossrefGoogle Scholar
  • Cascio E. U., Lewis E. G. Schooling and the armed forces qualifying test: Evidence from school-entry laws. J. Human Resources (2006) 41(2):294–318CrossrefGoogle Scholar
  • Dehejia R. Practical propensity score matching: A reply to smith and Todd. J. Econometrics (2005) 125(1–2):355–364CrossrefGoogle Scholar
  • Dehejia R. H., Wahba S. Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. J. Amer. Statist. Assoc. (1999) 94(448):1053–1062CrossrefGoogle Scholar
  • Dehejia R. H., Wahba S. Propensity score-matching methods for nonexperimental causal studies. Rev. Econom. Statist. (2002) 84(1):151–161CrossrefGoogle Scholar
  • DiNardo J. E., Lee D. S. Economic impacts of new unionization on private sector employers: 1984–2001. Quart. J. Econom. (2004) 119(4):1383–1441CrossrefGoogle Scholar
  • DiNardo J. E., Pischke J. S. The returns to computer use revisited: Have pencils changed the wage structure too? Quart. J. Econom. (1997) 112(1):291–303CrossrefGoogle Scholar
  • Ferratt T. W., Agarwal R., Brown C. V., Moore J. E. IT human resource management configurations and IT turnover: Theoretical synthesis and empirical analysis. Inform. Systems Res. (2005) 16(3):237–255LinkGoogle Scholar
  • Gerhart B., Rynes S. L.Compensation: Theory, Evidence, and Strategic Implications (2003) (Sage Publications, Thousand Oaks, CA) Google Scholar
  • Glymour C. Comment: Statistics and metaphysics. J. Amer. Statist. Assoc. (1986) 81(396):964–966Google Scholar
  • Greene W. H.Econometric Analysis (2000) 4th ed.(Prentice Hall, Upper Saddle River, NJ) Google Scholar
  • Gregor S. The nature of theory in information systems. MIS Quart. (2006) 30(3):611–642CrossrefGoogle Scholar
  • Hahn J., Klauww W. v. d., Todd P. Identification of treatment effects by regression discontinuity design. Econometrica (2001) 69(1):201–209CrossrefGoogle Scholar
  • Heckman J. J. Dummy endogenous variables in a simultaneous equation system. Econometrica (1978) 46(4):931–961CrossrefGoogle Scholar
  • Heckman J. J. Instrumental variables: A study of implicit behavioral assumptions used in making program evaluations. J. Human Resources (1997) 32(3):441–462CrossrefGoogle Scholar
  • Heckman J. Causal parameters and policy analysis in economics: A twentieth century retrospective. Quart. J. Econom. (2000) 115(1):45–97CrossrefGoogle Scholar
  • Heckman J. J. The scientific model of causality. Sociol. Methodology (2005) 35(1):1–150CrossrefGoogle Scholar
  • Heckman J. J., Robb R., Wainer H. Alternative methods for solving the problem of selection bias in evaluating the impact of treatments on outcomes. Drawing Inferences from Self-Selected Samples (1986) (Springer-Verlag, New York) 63–113CrossrefGoogle Scholar
  • Heckman J. J., Robb R., Duncan G., Kalton G. The value of longitudinal data for solving the problem of selection bias in evaluating the impact of treatment on outcomes. Panel Surveys (1988) (John Wiley, New York) 512–538Google Scholar
  • Heckman J. J., Ichimura H., Todd P. Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Rev. Econom. Stud. (1997) 64(4):605–654CrossrefGoogle Scholar
  • Heckman J. J., Ichimura H., Smith J., Todd P. Characterizing selection bias using experimental data. Econometrica (1998a) 66(5):1017–1098CrossrefGoogle Scholar
  • Heckman J. J., Ichimura H., Todd P. Matching as an econometric evaluation estimator. Rev. Econom. Stud. (1998b) 65(2):261–294CrossrefGoogle Scholar
  • Hirano K., Imbens G. W., Ridder G. Efficient estimation of average treatment effects using the estimated propensity score. Econometrica (2003) 71(4):1161–1189CrossrefGoogle Scholar
  • Hitt L. M., Wu D. J., Zhou X. Investments in enterprise resource planning: Business impact and productivity measures. J. Management Inform. Systems (2002) 19(1):71–98CrossrefGoogle Scholar
  • Holland P. W. Statistics and causal inference (with discussion). J. Amer. Statist. Assoc. (1986) 81(396):945–970CrossrefGoogle Scholar
  • Holland P. W. Causal inference, path analysis, and recursive structural equation models. Sociol. Methodology (1988) 18(1):449–484CrossrefGoogle Scholar
  • Holland P. W., Galavotti M. C., Suppes P., Costantini D. The causal interpretation of regression coefficients. Stochastic Causality (2001) (CSLI Publications, Stanford, CA) 173–187Google Scholar
  • Imai K., van Dyk D. A. Causal inference with generalized treatment regimes: Generalizing the propensity score. J. Amer. Statist. Assoc. (2004) 99(467):854–866CrossrefGoogle Scholar
  • Josefek R. A. J., Kauffman R. J. Nearing the threshold: An economics approach to presure on information systems professionals to separate from their employer. J. Management Inform. Systems (2003) 20(1):87–122CrossrefGoogle Scholar
  • LaLonde R. J. Evaluating the econometric evaluations of training programs with experimental data. Amer. Econom. Rev. (1986) 76(4):604–620Google Scholar
  • Lee B., Barua A., Whinston A. B. Discovery and representation of causal relationships in MIS research: A methodological framework. MIS Quart. (1997) 21(1):109–136CrossrefGoogle Scholar
  • Levina N., Xin M. Comparing IT worker's compensation across country contexts: Demographic, human capital, and institutional factors. Inform. System Res. (2007) 18(2):193–210LinkGoogle Scholar
  • Little R. J., Rubin D. Causal effects in clinical and epidemiological studies via potential outcomes: Concepts and analytical approaches. Ann. Rev. Public Health (2000) 21:121–145CrossrefGoogle Scholar
  • Lucas H. C., Banker R. D., Kauffman R. J., Mahmood M. A. The business value of information technology: A historical perspective and thoughts for future research. Strategic Information Technology Management: Perspectives on Organizational Growth and Competitive Advantage (1993) (Idea Group Publishing, Harrisburg, PA) 359–374Google Scholar
  • Mithas S. Are emerging markets different from developed markets? Human capital, sorting and segmentation in compensation of information technology professionals. Proc. 29th Internat. Conf. Inform. Systems (2008) Association for Inforamtion Systems, Paris, FranceGoogle Scholar
  • Mithas S., Krishnan M. S. Human capital and institutional effects in the compensation of information technology professionals in the United States. Management Sci. (2008) 54(3):415–428LinkGoogle Scholar
  • Mithas S., Lucas H. C. Does high-skill immigration make everyone better off? United States' visa policies and compensation of American and foreign information technology professionals. (2008) . Working paper, Robert H. Smith School of Business, University of Maryland, College ParkGoogle Scholar
  • Mithas S., Almirall D., Krishnan M. S. Do CRM systems cause one-to-one marketing effectiveness? Statist. Sci. (2006) 21(2):223–233CrossrefGoogle Scholar
  • Mithas S., Jones J. L., Mitchell W. Buyer intention to use Internet-enabled reverse auctions? The role of asset specificity, product specialization, and non-contractibility. MIS Quart. (2008) 32(4):705–724CrossrefGoogle Scholar
  • Mithas S., Krishnan M. S., Fornell C. Why do customer relationship management applications affect customer satisfaction? J. Marketing (2005) 69(4):201–209CrossrefGoogle Scholar
  • Mithas S., Ramasubbu N., Krishnan M. S., Fornell C. Designing websites for customer loyalty: A multilevel analysis. J. Management Inform. Systems (2006–07) 23(3):97–127CrossrefGoogle Scholar
  • Mithas S., Tafti A. R., Bardhan I. R., Goh J. M. Resolving the profitability paradox of information technology: Mechanisms and empirical evidence. (2007) . Working paper, Robert H. Smith School of Business, University of Maryland, College Park, available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1000732Google Scholar
  • Neyman J. S., Dabrowska D. M., Speed T. P. On the application of probability theory to agricultural experiments. Essay on principles. Statist. Sci. (1990) 5(4):465–472Section 9CrossrefGoogle Scholar
  • Pearl J.Causality: Models, Reasoning, and Inference (2000) (Cambride University Press, Cambridge, UK) Google Scholar
  • Porter M. E. From competitive advantage to corporate strategy. Harvard Bus. Rev. (1987) May–June):43–59Google Scholar
  • Pratt J. W., Schlaifer R. On the interpretation and observation of laws. J. Econometrics (1988) 39(1–2):23–52CrossrefGoogle Scholar
  • Ramasubbu N., Mithas S., Krishnan M. S., Kemerer C. F. Work dispersion, process-based learning, and offshore software development performance. MIS Quart. (2008) 32(2):437–458CrossrefGoogle Scholar
  • Ray G., Barney J. B., Muhanna W. A. Capabilities, business processes, and competitive advantage: Choosing the dependent variable in empirical tests of the resource-based view. Strategic Management J. (2004) 25(1):23–37CrossrefGoogle Scholar
  • Ray G., Muhanna W. A., Barney J. B. Information technology and the performance of the customer service process: A resource-based analysis. MIS Quart. (2005) 29(4):625–652CrossrefGoogle Scholar
  • Robins J. M., Hernan M. A., Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology (2000) 11:550–560CrossrefGoogle Scholar
  • Rosenbaum P. Choice as an alternative to control in observational studies. Statist. Sci. (1999) 14(3):259–304CrossrefGoogle Scholar
  • Rosenbaum P.Observational Studies (2002) 2nd ed.(Springer, New York) CrossrefGoogle Scholar
  • Rosenbaum P. R. Sensitivity analysis for certain permutation inferences in matched observational studies. Biometrika (1987) 74(1):13–26CrossrefGoogle Scholar
  • Rosenbaum P. R., Rubin D. B. Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. J. Roy. Statist. Soc. Ser. B (1983a) 45(2):212–218Google Scholar
  • Rosenbaum P. R., Rubin D. B. The central role of the propensity score in observational studies for causal effects. Biometrika (1983b) 70(1):41–55CrossrefGoogle Scholar
  • Rosenbaum P. R., Rubin D. B. Reducing bias in observational studies using subclassification on the propensity score. J. Amer. Statist. Assoc. (1984) 79(387):516–524CrossrefGoogle Scholar
  • Rubin D. B. Estimating causal effects from large data sets using propensity scores. Ann. Internal Medicine (1997) 127(8, Part 2):757–763CrossrefGoogle Scholar
  • Rubin D. B. Direct and indirect causal effects via potential outcomes. Scandinavian J. Statist. (2004) 31(2):161–170CrossrefGoogle Scholar
  • Rubin D. B. Causal inference using potential outcomes: Design, modeling, decisions. J. Amer. Statist. Assoc. (2005) 100(469):322–331CrossrefGoogle Scholar
  • Sambamurthy V., Bharadwaj A., Grover V. Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms. MIS Quart. (2003) 27(2):237–263CrossrefGoogle Scholar
  • Smaltz D., Sambamurthy V., Agarwal R. The antecedents of CIO role effectiveness in organizations: An empirical study in the healthcare sector. IEEE Trans. Engrg. Management (2006) 53(2):207–222CrossrefGoogle Scholar
  • Smith J. A., Todd P. E. Does matching overcome LaLonde's critique of nonexperimental estimators? J. Econometrics (2005a) 125(1–2):305–353CrossrefGoogle Scholar
  • Smith J. A., Todd P. E. Rejoinder. J. Econometrics (2005b) 125(1–2):365–375CrossrefGoogle Scholar
  • Whitaker J., Mithas S., Krishnan M. S. A field study of RFID deployment and return expectations. Production Oper. Management (2007) 16(5):599–612CrossrefGoogle Scholar
  • Winship C., Mare R. Models for sample selection bias. Ann. Rev. Sociol. (1992) 18:327–350CrossrefGoogle Scholar
  • Winship C., Morgan S. L. The estimation of causal effects from observational data. Ann. Rev. Sociol. (1999) 25:659–706CrossrefGoogle Scholar
  • Winship C., Sobel M., Hardy M., Bryman A. Causal inference in sociological studies. Handbook of Data Analysis (2004) (Sage, London) 481–503CrossrefGoogle Scholar
  • Xie Y., Wu X. Market premium, social process, and statisticism. Amer. Sociol. Rev. (2005) 70(5):865–870CrossrefGoogle 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.