Propensity Score Matching (PSM) Quasi-Experiment
A Propensity Score Matching (PSM) Quasi-Experiment is a statistical matching technique that ... propensity score.
- See: Matched-Control Quasi-Experiment, Propensity Score, Multivariate Analysis, Matching (Statistics), Estimation Theory.
References
2020
- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Propensity_score_matching Retrieved:2020-10-9.
- In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. PSM attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among units that received the treatment versus those that did not. Paul R. Rosenbaum and Donald Rubin introduced the technique in 1983.[1]
The possibility of bias arises because a difference in the treatment outcome (such as the average treatment effect) between treated and untreated groups may be caused by a factor that predicts treatment rather than the treatment itself. In randomized experiments, the randomization enables unbiased estimation of treatment effects; for each covariate, randomization implies that treatment-groups will be balanced on average, by the law of large numbers. Unfortunately, for observational studies, the assignment of treatments to research subjects is typically not random. Matching attempts to reduce the treatment assignment bias, and mimic randomization, by creating a sample of units that received the treatment that is comparable on all observed covariates to a sample of units that did not receive the treatment.
For example, one may be interested to know the consequences of smoking. An observational study is required since it is unethical to randomly assign people to the treatment 'smoking.' The treatment effect estimated by simply comparing those who smoked to those who did not smoke would be biased by any factors that predict smoking (e.g.: gender and age). PSM attempts to control for these biases by making the groups receiving treatment and not-treatment comparable with respect to the control variables.
- In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. PSM attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among units that received the treatment versus those that did not. Paul R. Rosenbaum and Donald Rubin introduced the technique in 1983.[1]
- ↑ Cite error: Invalid
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2016
- (Johansson et al., 2016) ⇒ Fredrik D. Johansson, Uri Shalit, and David Sontag. (2016). “Learning Representations for Counterfactual Inference.” In: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48.
- QUOTE: ... Counterfactual inference for determining causal effects in observational studies has been studied extensively in statistics, economics, epidemiology and sociology (Morgan & Winship, 2014; Robins et al., 2000; Rubin, 2011; Chernozhukov et al., 2013) as well as in machine learning (Langford et al., 2011; Bottou et al., 2013; Swaminathan & Joachims, 2015a).
Non-parametric methods do not attempt to model the relation between the context, intervention, and outcome. The methods include nearest-neighbor matching, propensity score matching, and propensity score re-weighting (Rosenbaum & Rubin, 1983; Rosenbaum, 2002; Austin, 2011). Parametric methods, on the other hand, attempt to concretely model the relation between the context, intervention, and outcome. These methods include any type of regression including linear and logistic regression (Prentice, 1976; Gelman & Hill, 2006), random forests (Wager &Athey, 2015) and regression trees (Chipman et al., 2010). ...
- QUOTE: ... Counterfactual inference for determining causal effects in observational studies has been studied extensively in statistics, economics, epidemiology and sociology (Morgan & Winship, 2014; Robins et al., 2000; Rubin, 2011; Chernozhukov et al., 2013) as well as in machine learning (Langford et al., 2011; Bottou et al., 2013; Swaminathan & Joachims, 2015a).
2011
- (Biondi-Zoccai et al., 2011) ⇒ Giuseppe Biondi-Zoccai, Enrico Romagnoli, Pierfrancesco Agostoni, Davide Capodanno, Davide Castagno, Fabrizio D'Ascenzo, Giuseppe Sangiorgi, and Maria Grazia Modena. (2011). “Are Propensity Scores Really Superior to Standard Multivariable Analysis?." In: Contemporary Clinical Trials, 32(5). doi:10.1016/j.cct.2011.05.006
2000
- (Imbens, 2000) ⇒ Guido W. Imbens. (2000). “The Role of the Propensity Score in Estimating Dose-response Functions.” Biometrika 87, no. 3
- ABSTRACT: Estimation of average treatment effects in observational studies often requires adjustment for differences in pre-treatment variables. If the number of pre-treatment variables is large, standard covariance adjustment methods are often inadequate. Rosenbaum & Rubin (1983) propose an alternative method for adjusting for pre-treatment variables for the binary treatment case based on the so-called propensity score. Here an extension of the propensity score methodology is proposed that allows for estimation of average casual effects with multi-valued treatments.