A unified framework for bounding causal effects on the always-survivor and other populations (2403.13398v2)
Abstract: We investigate the bounding problem of causal effects in experimental studies in which the outcome is truncated by death, meaning that the subject dies before the outcome can be measured. Causal effects cannot be point identified without instruments and/or tight parametric assumptions but can be bounded under mild restrictions. Previous work on partial identification under the principal stratification framework has primarily focused on the always-survivor' subpopulation. In this paper, we present a novel nonparametric unified framework to provide sharp bounds on causal effects on discrete and continuous square-integrable outcomes. These bounds are derived on thealways-survivor', protected', andharmed' subpopulations and on the entire population with/without assumptions of monotonicity and stochastic dominance. The main idea depends on rewriting the optimization problem in terms of the integrated tail probability expectation formula using a set of conditional probability distributions. The proposed procedure allows for settings with any type and number of covariates, and can be extended to incorporate average causal effects and complier average causal effects. Furthermore, we present several simulation studies conducted under various assumptions as well as the application of the proposed approach to a real dataset from the National Supported Work Demonstration.
- Identification of causal effects using instrumental variables. Journal of the American Statistical Association 434, 444–455.
- Bounds on treatment effects from studies with imperfect compliance. Journal of the American Statistical Association 92, 1171–1176.
- Partial identification using random set theory. Journal of Econometrics 166, 17–32.
- Bounds on average and quantile treatment tffects on duration outcomes under censoring, selection, and noncompliance. Journal of Business & Economic Statistics 38, 901–920.
- Bounds on quantile treatment effects of job corps on participants’ wages. Journal of Human Resources 48, 659–701.
- Changes in the distribution of male and female wages accounting for employment composition using bounds. Econometrica 75, 323–363.
- Bounds on direct effects in the presence of confounded intermediate variables. Biometrics 64, 695–701.
- Bounds on treatment effects in the presence of sample selection and noncompliance: The wage effects of job corps. Journal of Business & Economic Statistics 33, 523–540.
- Identifiability and estimation of causal effects by principal stratification with outcomes truncated by death. Journal of the American Statistical Association 106, 1578–1591.
- Die maximierung eines quotienten zweier linearer funktionen unter linearen nebenbedingungen. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete 1, 141–145.
- Principal stratification in causal inference. Biometrics 58, 21–29.
- Large sample bounds on the survivor average causal effect in the presence of a binary covariate with conditionally ignorable treatment assignment. The International Journal of Biostatistics 10, 143–63.
- Causal bounds for outcome-dependent sampling in observational studies. Journal of the American Statistical Association 117, 939–950.
- A path-sampling method to partially identify causal effects in instrumental variable models. arXiv preprint arXiv:1910.09502 .
- Instrumental variables, selection models, and tight bounds on the average treatment effect. Econometric Evaluaton of Labour Market Policies 13, 1–15.
- Sharp iv bounds on average treatment effects on the treated and other populations under endogeneity and noncompliance. Journal of Applied Econometrics 32, 56–79.
- Sharp bounds on causal effects under sample selection. Oxford Bulletin of Economics and Statistics 77, 129–151.
- Sharp bounds on the causal effects in randomized experiments with truncation-by-death. Statistics & Probability Letters 78, 144–149.
- Survivor-complier effects in the presence of selection on treatment with application to a study of prompt icu admission. Journal of the American Statistical Association 114, 93–104.
- The identification region of the potential outcome distributions under instrument independence. Journal of Econometrics 225, 231–253.
- LaLonde, 1986. Evaluating the econometric evaluation of training program with experimental data. The American Economic Review 76, 604–620.
- Training, Wages, and Sample Selection: Estimating Sharp Bounds on Treatment Effects. The Review of Economic Studies 76, 1071–1102.
- Nonparametric estimation of conditional cdf and quantile functions with mixed categorical and continuous data. Journal of Business & Economic Statistics 26, 423–434.
- Demystifying the integrated tail probability expectation formula. The American Statistician 73, 367–374.
- Causal inference with outcomes truncated by death in multiarm studies. Biometrics 79, 502–513.
- Nonparametric bounds on treatment effects. The American Economic Review 80, 319–323.
- Partial identification of probability distributions. Springer.
- Understanding and using linear programming. Springer.
- Comparison methods for stochastic models and risks. Technometrics 45, 370–371.
- Causality: Models, reasoning, and inference. 2 ed., Cambridge University Press.
- Analysis of the binary instrumental variable model. Heuristics, Probability and Causality: A Tribute to Judea Pearl 25, 415–444.
- Randomization analysis of experimental data: The fisher randomization test comment. Journal of the American Statistical Association 75, 591–593.
- A general method for deriving tight symbolic bounds on causal effects. Journal of Computational and Graphical Statistics 0, 1–10.
- Doubly robust difference-in-differences estimators. Journal of Econometrics 219, 101–122.
- Fractional programming. European Journal of Operational Research 12, 325–338.
- Sharp bounds on survivor average causal effects when the outcome is binary and truncated by death. ACM Transactions on Intelligent Systems and Technology 7, 1–11.
- Partial identification of the average treatment effect using instrumental variables: review of methods for binary instruments, treatments, and outcomes. Journal of the American Statistical Association 113, 933–947.
- Identification and estimation of survivor average causal effects. Statistics in Medicine 33, 3601–3628.
- Identification and estimation of causal effects with outcomes truncated by death. Biometrika 104, 597–612.
- Bounding causal effects on continuous outcome. Proceedings of the AAAI Conference on Artificial Intelligence 35, 12207–12215.
- Estimation of causal effects via principal stratification when some outcomes are truncated by “death”. Journal of Educational and Behavioral Statistics 28, 353–368.
- Evaluating the effects of job training programs on wages through principal stratification. Journal of the American Statistical Association 21, 117–145.
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