A recent topic of much interest in causal inference is model selection. data and imputation are drawn. A difference LASSO algorithm is defined along with its multiple imputation analogues. The procedures are illustrated using a well-known right heart catheterization dataset. to be the treatment indicator that takes values zero and one. The random variables Crocin II (0) (1) are the potential outcomes for the subject under = 0 and = 1 respectively. What we observe is ≡ = 1 … (0) and (1) can not be observed simultaneously i.e. one of them is missing. Two parameters of interest are the average causal effect: and the Crocin II potential outcomes {= 1|X). Rosenbaum and Rubin [7] show that if (3) holds the following property is also true: = 0 group and = 1 group. If we were to find the classifier based on that separates the = 1 and = 0 group it seen from Figure 1 that there is relatively limited covariate overlap between the two treatment groups which is a violation of the common support condition [7]. For example when < ?2 or > 4 the estimation of (1) ? (0) will be completely based on extrapolation. Intuitively the criterion for optimization in the propensity score model does not match up to the ultimate scientific goal which is “good” estimation of causal effects. This suggests that variable selection for the propensity score model is not sufficient for good causal effect estimation. Figure 1 Distribution of covariate for treatment and control groups. The blue line denotes the kernel density estimation for in the = 1 group while the magenta Crocin II line represents the kernel density estimate for in the = 0 group. The bars represent the … Similarly if we were to perform variable selection of the mean outcome model then this has problems as well. It represents the scientific model of interest and is intended to identify the causal estimands of interest. Performing variable selection on this model has problems in that different combinations of variables will correspond to different mean outcome models which naturally will change the scientific question of interest. This discussion is intended to explain why model/variable selection will not be straightforward in the potential outcomes framework. 2.3 Potential outcomes and prediction With the assumptions described in §2.1 we can characterize the joint Crocin II distribution of the counterfactuals. An alternative approach would be to begin with regression models for the potential outcomes such as the structural nested mean models (SNMs [22]) or marginal structural models (MSMs [23]). Note that SNMs and MSMs refer to model specification for the potential outcomes and define different target estimands of interest. This is a separate issue from the goal of are being made in the modelling of potential outcomes. Coming back to the original potential outcomes framework outlined in §2.1. the complete data consists of {= 1 … = = 1 … on and X Crocin II and suppose that the response variable is continuous. A standard penalized regression to fit would be the LASSO [9] which minimizes the residual sum of squares ≥ 0. Note that the constraint in (6) is on the here we are actually alluding to models for the joint distribution of the potential outcomes. The Mouse monoclonal to IgG2a Isotype Control.This can be used as a mouse IgG2a isotype control in flow cytometry and other applications. Kullback-Leibler distance is generically defined as and represent densities of the data. While the optimization problem in (7) is written down in a general form what Tran et al. [21] show is that in a linear model case it corresponds to solving a weighted LASSO problem of the form minimizing on and X. Based on the model fitted in step 1. compute predicted/fitted values of using the test dataset. Solve equation (8) using the test data. Again there is an implicit assumption that the training and test datsets will come from the same distribution. In the second step of the algorithm what is being computed Crocin II are empirical estimates of the mean of the posterior predictive distribution of given the observed covariate values. The predictive LASSO occurs in the third step of the algorithm. This reinterpretation also highlights the prediction done in the first two steps and the penalized regression in the third. It also immediately suggests extensions in which the first two steps are substituted with any arbitrary imputation algorithm. Examples of imputation methods include multiply imputed chained equations [27] and IVEWARE [28]. 3 Proposed methods 3.1 Adaptation to causal inference The application of the LASSO method for.