The probability of receiving a treatment given a set of observed covariates.

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33 views

Propensity Score Matching in R with Multiple Treatments

I commonly estimate Average effect of Treatment on the Treated (ATT) via Propensity Score Matching for a particular business use case. I'm currently using R, where ...
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0answers
16 views

Difference between marginal and conditional treatment effect? Relating to regression vs. propensity score methods

Peter Austin has a nice introduction to propensity score methods (citation below), and he states that one of the differences between PS methods and plain regression is that PS methods give you a ...
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1answer
37 views

Bayesian Network or Logistic regression?

The Bayesian Networks and Logistic regression can be used to predict events or give to each customer the propensity to have a behavior. Which are the advantages or disadvantages of these 2 methods? ...
3
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1answer
75 views

Propensity Score can be used as a covariate in regression?

I have treated and control groups with a problem of selection in the treatment group. I am interested in the identification of the following model: $y= exp(X^\prime\beta + \alpha\cdot T)$ where $T$ ...
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23 views

Observational data, matching, and resampling. Is this method defensible?

I am interested in comments on the validity (or not) of the following method for propensity score analysis. I’ll simplify the scenario a bit for clarity. I have 1000 subjects that received the ...
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0answers
28 views

Exclusion Restriction for Inverse Propensity Score Weighted Regression

I have been told that when fitting an inverse propensity score weighted regression 1) every control in the regression model should be used in the model estimating the propensity score, and 2) there ...
0
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0answers
26 views

Propensity score to match on exposure thats not a treatment

I have two questions on this subject: (1) The literature on propensity score (PS) consistently discusses the ability of PS to balance groups with different treatments. Does PS allow for balancing on ...
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0answers
59 views

PSM, Diff-in-Diff and Neg-logged income variable? How to interpret estimates?

I am estimating a difference-in-difference based on propensity score matching. The "treatment"-variable defines whether a household registered for a public insurance which was only active for two ...
1
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1answer
73 views

Interactions in Propensity Score Models

I am doing an analysis to see if a first-year seminar has an effect on student retention in college. Students choose whether or not to enroll in the seminar on their own, so it seems like it makes ...
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1answer
56 views

Nearest Neighbor Matching in R using matchit

I am using the matchit package to do propensity score matching on a data set. However, when doing nearest neighbor matching, if I use the caliper option, I get a different set of matched pairs every ...
0
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0answers
46 views

Propensity score matching by gender in stata

Hello Stack Community! I'm trying to do a matching in Stata. I need to do a propensity score matching between a single individuals dataset (men and women) to a couple dataset. What I like to do is to ...
2
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1answer
78 views

Propensity Score Analysis with continuous treatment

I have an observational dataset of about two dozen observed variables (continuous or discrete), plus a continuous variable of which I would like to measure the causal impact of on my dependent ...
0
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0answers
23 views

Multilevel propensity score

I'm trying to analyze many treatments on outcome after propensity score 1:1 matching. My problem: I have 6 differents drugs and each patient can take or not each of these. If I build my propensity ...
6
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2answers
172 views

Propensity Score Matching with time-varying treatment

The basic propensity score matching procedure works with cross-section data (ie collected at a certain point in time). The popular psmatch2 command uses a dummy variable indicating that an ...
0
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1answer
10 views

confound significantly different across groups

I feel this has to come up quite frequently, but there seems to be little clarity in how to handle the problem. If you have a demographic variable (e.g. age, edu) that significantly differs across ...
5
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5answers
287 views

Adjust for everything you have in propensity score?

I have a methodological question, and therefore no sample dataset is attached. I'm planning to do a propensity score adjusted Cox regression that aims to examine whether a certain drug will reduce ...
0
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1answer
93 views

Assessing quality of propensity score matching via covariate balance using standardised difference

I've been tasked with performing a propensity score matching and to measure the standardised difference for every covariate to assess the quality of fit. Details ...
3
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0answers
83 views

Propensity score matching: using alternative methods to create a distance measure

I would like to use a greedy nearest neighbour method to do propensity score matching. Though I've little experience here, it seems that the distance measure used is generally a propensity score ...
1
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0answers
53 views

Propensity score in time-updated (counting process) Cox regression

Suppose I aim to examine if treatment A is better than treatment B. My dataset consists of 10,000 individuals with a total of 100,000 observations. Thus each individual is observed 10 times and his ...
0
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0answers
109 views

propensity score with SPSS and R plug-in

I have recently installed R-essential and the "psmatching" program. All went well, until I performed (actually tried to perform) the analysis. Basically, I have a cohort of patients, divided into two ...
4
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1answer
93 views

Create age and sexmatched pairs to balance Cox regression further (updated)

I analyze ethnic differences in risk of cardiovascular events (CVD) in a cohort study of patients with coronary heart disease. It is known that immigrants have higher risk of CVD and I intend to show ...
0
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1answer
221 views

What is the equation for random forest?

I need an equation for random forest so that I can score fresh data I receive every week, based on beta estimates I got after building model using this ensemble methodology. Every week I do not want ...
1
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1answer
250 views

Accounting for Violation of Parallel Trend Assumption in Diff-in-Diff with Propensity-Score Matching

Objective: I want to test the effect of a regulatory change using a classical pre-post/treatment-control DiD design (Y = POST + TREAT + POSTxTREAT + e). Problem: Treatment & control obs. are from ...
5
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0answers
102 views

Propensity Score Matching – How do the mechanics lead to a different result than unmatched?

The gist of propensity score matching, as I understand it, is as follows: You want to estimate the average treatment effect (ATE) of a treatment on some outcome. However, if you simply calculate the ...
0
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0answers
38 views

Meta-analysis with known confounder

I’m performing a meta-analysis in which the main outcome of interest is a correlation coefficient between two variables, $X$ (a psychological measure) and $Y$ (a biomarker). $Y$ is known to be ...
3
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1answer
138 views

Infer causality with high collinearity

I recently started to ask myself how to measure the impact of education on indexes like GDP: what is the outcome of mathematics or computer science on GDP, at the country level for instance. In this ...
1
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1answer
134 views

Propensity score matching with multiple treatments

Is anyone aware of propensity score matching methods for when there are more than 2 treatment groups? I am working on a project with 4 treatment groups: A B A and B Neither A nor B Calculating ...
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0answers
265 views

Simulate predicted probabilities after a logistic regression

I'm generating predicted probabilities (propensity scores) in Stata using the predict command. The basic structure of my code looks like this: ...
2
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0answers
57 views

Statistical thought experiment (possibly Bayesian) about survey sampling and propensity scores

For some practical application I recently came about the following thought experiment. Can anybody help? Suppose we administer a survey A to measure a variable $Y$. The response probabilities may ...
5
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1answer
64 views

Accounting for post-intervention bias following propensity matching

Using a large, cross-sectional survey of victims of violence, I am interested in testing the effect of alcohol intoxication (let's call it the 'treatment') on victims' subjective rating of the ...
1
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2answers
119 views

Propensity Score

What are the various methods used for binary classification other than logistic regression? What are the advantages of logistic reg. model in developing Propensity score w.r.t. other methods? ...
1
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0answers
72 views

Propensity scores and patient comparability

I have two groups of patients who underwent a surgery using method A or method B. The first group are patients who were operated in 1980's and 1990's only with method A. The second group are patients ...
2
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0answers
275 views

Propensity score and Cox regression

I have a retrospective dataset of patients treated with a certain drug (treatment, $n=46$) or with placebo (control $n=96$). The stored variables are age, sex, stage of disease. I want to assess the ...
2
votes
2answers
211 views

Propensity score matching with zip code/geographical distance

I have a test group and a control group with a bunch of covariates; one of them is ZIP code. Is there a methodology that I can use to perform a propensity score matching based on ZIP code or other ...
1
vote
1answer
234 views

Understanding output of MatchIt in R

I have created a Matched Cohort using MatchIt package in R. I have the list of members who are in the treatment group and the control group. But I am unable to figure out which treatment subject is ...
1
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1answer
195 views

Propensity score matching using R

I have 50 control subjects and I would like to get 150 treatment subjects (i.e., a 1:3 ratio between the control and treatment groups) using propensity score matching and a few covariates. After ...
6
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3answers
7k views

Propensity score matching with panel data

I have a longitudinal data set of individuals and some of them were subject to a treatment and others were not. All individuals are in the sample from birth until age 18 and the treatment happens at ...
3
votes
1answer
93 views

Multiple imputation for variables used to calculate regression weights

My basic question: is there anything that you can't impute using MI? My more complicated question: Consider the regression $Y=\rho T+X'\beta+\epsilon$. For whatever reason, you want to weight the ...
2
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0answers
72 views

Heterogeneous Treatment Effects - How to test differences in the ATE?

I want to conduct a simple propensity score estimation where the treatment $D_i$ is a binary variable ($D_i=1$ individual $i$ participates in the labor market program, zero otherwise). I estimate the ...
2
votes
2answers
358 views

reconstructing matched data set from R's matching package

I am using Mebane and Sekhon's Matching package for R, and my goal is to make casual inferences about a proportion and a mean. The proportion is straight forward, but the mean has missing values, ...
10
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2answers
994 views

Propensity score weighting in Cox PH analysis and covariate selection

Regarding propensity score weighting (IPTW) when doing Cox proportional hazard modeling of time-to-event survival data: I have prospective registry data where we're interested in looking at treatment ...
1
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1answer
205 views

Using propensity scores from twang in coxph

I've used the twang package to calculate propensity score weights in my observational data set. The twang vignette uses the ...
4
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2answers
163 views

Is matching only for treatment effects with selection bias?

Propensity score matching (and other matching techniques) are used, as far as I have seen, exclusively for identifying causal effects of a treatment (intervention) and particularly where there is a ...
19
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3answers
2k views

Propensity score matching after multiple imputation

I refer to this paper: Hayes JR, Groner JI. "Using multiple imputation and propensity scores to test the effect of car seats and seat belt usage on injury severity from trauma registry data." J ...
2
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1answer
81 views

Account for group age differences

I have a dataset of two patient and one healthy control group which I would like to compare (using R) with respect to a continuous outcome variable (each subjects is measured once). However the groups ...
1
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2answers
160 views

How is it possible to turn out with a highly accurate prediction when all records were classified the same way?

We ran a CHAID decision tree model using the set up and process described in my related question here. We used the propensity scores to come up with a prediction. We measured the prediction at the end ...
1
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0answers
166 views

Predicting response propensity in a rolling data collection

I'm working with a survey that uses a rolling data collection format (i.e., there are multiple waves of sampling and initial contacts). I'm trying to develop a model to predict how likely a sample ...
3
votes
1answer
252 views

How to perform 1-to-1 matching when there are more treated than control subjects?

When there are more treated than control, is it possible to do 1-to-1 matching (without replacement)? It makes sense to do 1-to-1 when there are less treated than control but I am not sure if one can ...
2
votes
1answer
327 views

Is it appropriate to calculate odds ratios between groups matched on covariates (propensity scores)?

I have a data set that includes a primary dichotomous independent variable (e.g., smoking), a primary dichotomous dependent variable (e.g., chronic back pain), and several covariates (e.g., diagnosis ...
6
votes
2answers
2k views

Use of propensity score in a case-control study

The theory of propensity score (PS) suggests that it should only be used for the cohort study because PS matches the "treated/exposed" to the "un-treated/un-exposed" groups. However, cases and ...