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

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7 views

Inverse probability weighting (IPW): standard errors after weighting observations

When using propensity scores for inverse probability weighting (IPW) the standard errors for the parameters in the regression model may be affected. I have seen several examples of people using ...
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9 views

matching methods when treated group is much larger than untreated group

I have a treatment group with Nt = 2000 and an untreated group with Nu=500. I want to perform propensity score matching/weighting (PSM). It seems like the common matching methods have serious flaws ...
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7 views

Propensity score matching for “large” datasets - smarter solution than nearest neighbour?

The following question has some programming / software package as well as some numerical aspects, but I would consider it a statistical question at heart - I hope you agree. I am trying to do 1:1 ...
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15 views

Propensity score methods versus GLM for cost comparisons between treatment groups

I'm working with a client that wants cost differences between two treatment groups. They have little background knowledge but have seen in the literature folks doing GLM gamma/log-link, etc. and are ...
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11 views

Quasi-experiment with naturally assembled collectives (classrooms): Propensity score matching or not?

I am planning on running a quasi-experiment (pre-test post-test non-equivalent groups design) that involves two treatment groups that are subjected to different treatments (X+ & X-) and one ...
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1answer
36 views

Propensity Score Matching

What is the right approach when there is a variable that does not affect participation but effects the outcome measure? I have data set of the health outcome of a treatment and control group. I have ...
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19 views

Propensity Score Matching and significance of estimators in logit regression

If I run a logistic regression to calculate a propensity score (i.e. the dependent variable is 0= do not participate in the treatment; 1= do participate) and therefore I want to see the probability a ...
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1answer
18 views

What type of matching should I use?

I am conducting a retrospective cohort analysis looking at major psych diagnosis (exposure) on outcomes following traumatic injury using a large registry. n exposed = 36,000 ; n unexposed = 3.5 ...
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11 views

Propensity score matching: gain or loss in degree of freedom?

I saw some argument in a thread that propensity score matching (PSM) has statistical advantage over OLS in terms of degree of freedom. http://stats.stackexchange.com/a/8610/78057 However, my ...
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48 views

propensity score matching and survival analysis

I am using the Matching R package to compute Average Treatment Effect within Propensity Score Matching (PSM). I have two groups: treatment and control. In ...
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1answer
40 views

Inverse probability weighting in logistic models - large weights irrelevant when using additional covariates?

I am using propensity scores for IPW in a logistic GLM in R. Two of the propensities are quite small and thus the resulting weights are quite large - much larger than all the others. I expected these ...
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1answer
47 views

Post propensity score matching analysis

I want to look into the causal effects of an education program on a binary employment outcome (positive vs negative). I have two groups of students- one that took the education program (the treatment ...
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50 views

propensity score matching and difference in difference

I am trying to analyse the impact of a cash stipend program, onto child learning outcomes. I have first modeled the conditional probability of each student receiving this program. So basically I have ...
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1answer
45 views

How to do propensity score matching in R on a dataset of 10 million patients (won't fit into RAM)? [closed]

I need to do some propensity score matching. My dataset is way too large to fit into RAM, which I see as R's biggest problem right now. I see that the ff and ffbase packages are what I need. I ...
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0answers
31 views

Can anyone help interpreting this equation?

The following is a portion of text from the Heckman, Ichimura and Todd (1997) paper about propensity score matching in ReStud The matching methods discussed in this paper focus on estimating an ...
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1answer
62 views

How to create matched sample for sample selection method to perform?

I'm working on a project in which I only use sub sample (say the immigrant households of all households), and there is a sample selection problem that I apply the standard Heckman's sample selection ...
3
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1answer
164 views

Survival analysis: Multiply impute 5 datasets to average one propensity score then analyze OR pool the estimate from 5 imputed datasets?

I have a question regarding the use of propensity score in a survival analysis with use of mutliple imputation to handle missing data. The question is of theoretical nature and may well apply to other ...
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1answer
61 views

Combining propensity score matching with 2SLS

Inspired by the probit 2SLS estimation (see e.g. Wooldridge p.623, procedure 18.1 or check here probit two stage least squares), I am wondering if instead of running a Probit in the very first step, I ...
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96 views

Conducting Analysis after Propensity Score Matching

I want to perform propensity score matching of observational data of an Intensive Care Unit in order to find out wheather hydroxyethyl starch is better or worse than colloids in terms of renal ...
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20 views

For which data can propensity score matching be applied?

I wondered if besides controlled experiments (random distribution of treatments) and observational studies (treatment for homogeneous individuals), other settings apply for propensity score matching. ...
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127 views

Propensity score nearest neighbor matching with replacement and caliper

I am using the package MatchIt in R to perform propensity score matching. I have chosen to use nearest neighbor matching with a caliper of 0.2 and since in my case i have more cases than controls i ...
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30 views

propensity score matching: non-standard scenario

I have a time series of web transactions from a sequential test, i.e. there was no control. In other words I have the number of transactions before and after a change and want to evaluate the effect ...
5
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1answer
347 views

Confidence interval for average treatment effect from propensity score weighting?

I am trying to estimate the average treatment effect from observational data using propensity score weighting (specifically IPTW). I think I am calculating the ATE correctly, but I don't know how to ...
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127 views

Paired or not paired? Comparing groups after propensity score matching

After matching on propensity score, e.g 1:1 matching, you obtain a matched subset of your data. The built-in functions in the Matching package, as a prominent example, compares groups before matching ...
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119 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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80 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
70 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? ...
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1answer
113 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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58 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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32 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 ...
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47 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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105 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 ...
2
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1answer
189 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
333 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 ...
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99 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 ...
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1answer
383 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 ...
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35 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 ...
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2answers
446 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 ...
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1answer
12 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 ...
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5answers
727 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 ...
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1answer
238 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 ...
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0answers
127 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 ...
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0answers
123 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 ...
4
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1answer
194 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 ...
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1answer
514 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 ...
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1answer
714 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 ...
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141 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 ...
3
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1answer
178 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 ...
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1answer
167 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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396 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: ...