Questions tagged [propensity-scores]
The probability of receiving a treatment given a set of observed covariates.
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questions
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1answer
15 views
Causal inference for additive multiple treatments
I encountered a causal inference problem in practice and want to find if there is a previously established statistical toolset that can be applied to my problem.
My problem is characterized as ...
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1answer
20 views
Low propensity score among treated and high among the control units
Is it conceivable for the treated units to have propensity scores systematically lower than those estimated for the control units?
Thanks in advance.
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1answer
14 views
r propensity score matching (psm) for the same year [closed]
I am conducting PSM for my study.
I used MatchIt package for my PSM.
And the result gave me a similar set of control groups, but they control groups were different from the treatment group in terms ...
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0answers
15 views
Uplift modelling strategy and Impact Analysis
Overview:
There are customers who are getting disabled and enabled everyday in the portfolio. Disablement essentially means, when a customer had paid for few days worth of subscription the end of the ...
2
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1answer
28 views
Is it possible of overfit using Propensity score matching with the MatchIt R package?
I have a very large patient cohort and I am trying to define cases and controls whilst minimizing selection bias. Further down the line, I am using Cox regression to assess the efficacy of particular ...
1
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1answer
27 views
hypotheis testing in observational study with propensity score matching to reduce confounding
in observational studies, many people use propensity score matching to reduce confounding (measured co-voriates) between two groups (cohorts). But due to some unobserved confounding co-variates (not ...
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31 views
match on a variable that itself depends on matching
I want to determine whether Program X improved graduation rates at a higher ed institution. Students self-select into this program, so I'm using propensity score matching (with the ...
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0answers
20 views
Is there a way to evaluate the accuracy or misclassification rate of the linear model that the R package MatchIt uses to build propensity scores? [closed]
m1.out <- matchit(Treatment ~ co_variate_1 + co_variate_2 + co_variate_3,
data = mydata, method = "nearest", ratio = 1)
summary(m1.out)
I am ...
2
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1answer
38 views
Does a doubly robust estimator magnify bias if *both* the outcome regression and inverse propensity score weighting are incorrect models?
The doubly robust estimator is a popular method for measuring the average treatment effect with observational data (assuming no unmeasured confounders):
$$ \hat{\Delta}_{DR} = n^{-1}\sum_{i=1}^n \...
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0answers
52 views
Propensity Score Matching with Cox Regression
I am conducting a survival analysis with a Cox regression whereby the outcome variable (promotion to a senior role) is either 0 or 1. I am particulalry interested in the hazard rate (i.e., the 'hazard'...
1
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0answers
17 views
Propensity score matching in r using panel data [closed]
I want to conduct PSM using firm panel data in r.
matchit(treat ~ leverage + cash + roa + mtb + asset, data=data)
This gave me a result of only very similar one ...
1
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1answer
66 views
advantages and disadvantages of IPTW vs propensity score matching?
what are the advantages and disadvantages of IPTW (Inverse Probability of Treatment Weighting) comparing to PSM (propensity score matching) in dealing with confounding variables?
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2answers
44 views
Favored methods for overcoming selection bias (special attention to healthcare fields)?
I am frequently measuring the effect of behavioral health treatment interventions on outcomes of interest. However, comparing the relative efficacy of different types of treatment is tricky - more ...
2
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1answer
42 views
Using IPS(inverse probability weighting) with a deterministic policy as the logging policy
In a contextual bandit problem, why can't we use inverse probability weighting (inverse propensity score) with a deterministic policy as the logging policy? Could you give me a concrete example?
2
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0answers
17 views
How to determine the propensity score classifier validity? [duplicate]
I've got a bit more background in machine learning than statistics. Let's say that I want to analyze causal effects based on propensity scores of the treatment and control group. I know that most ...
3
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1answer
104 views
Should I use a machine learning model to calculate propensity score?
In my study, running a simple linear model to calculate de propensity score for each example seemed to not be able to model my treatment choosing process correctly. My question is, does it make sense ...
1
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1answer
21 views
similar groups and need for propensity score matching?
If t-tests show that there is no significant difference between the control and treatment groups, is there a need to do a propensity score matching?
Thank you,
=sa
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1answer
68 views
Propensity score matching in SPSS
A practical question. When performing propensity score matching in SPSS v25, I get a separate sheet with all the cases and pairs. However, a small number of cases have propensity variable blank (10 of ...
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0answers
26 views
Causal Inference in a employee churn context (difference-in-differences / Propensity score matching)
For my master thesis I'm trying to determine the causes of an employee leaving a company. Currently I'm trying to study the effect that giving a raise has on employee leaving a company or not. So my ...
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0answers
16 views
In a regression to estimate propensity score, how can I build weights proportional to two different quantities?
I have a set of 88 people undergoing a treatment. My focus is on their contacts with a psychiatric service in the year before starting of the treatment, so I want an exact match wrt their previous ...
1
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0answers
33 views
Ok to use PSM to create treatment groups and then plug into CausalImpact? [closed]
Is it ok to use propensity score matching to create treatment and control groups and then plug these two time series into CausalImpact to estimate your treatment effect? I might want to do this, for ...
2
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1answer
53 views
Expectation of potential outcomes formula
In Mostly Harmless Econometrics, the author uses the following identity to derive an estimator for the causal effect:
$$E \left[ \frac{Y_i D_i} {p(X_i)} \right] = E \left[Y_{1i} \right]$$
where:
$...
0
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1answer
32 views
large variance from inverse probability weighting (inverse propensity score)
I heard if the observed data that will be used in the inverse probability weighting method is too small, the estimator based on the weighting will have a large variance.
Could you explain why that is ...
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0answers
32 views
Inverse Propensity Score in the paper “Doubly Robust Policy Evaluation and Optimization”
I am currently reading a paper whose link is https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/paper-14.pdf.
In the page 5, or 489, an estimation based on inverse propensity score ...
2
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0answers
19 views
Transfer Learning in domains other than Image processing and NLP
Can Transfer Learning be applied in domains other than Image processing or NLP? I am trying to apply it on clickstream data (for propensity modeling). Any reference would be greatly appreciated.
3
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2answers
163 views
Propensity scores in logistic regression models
I have a query after reading a paper, which is about the effectiveness of a medical device. In summary, what the authors did was
1. Generating a propensity score using a multivariable logistic ...
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0answers
105 views
Why do stabilized IPW weights give the same estimates and SEs as unstabilized weights?
In Cole & Hernán (2008), the authors mention that using stabilized weights can decrease the variance of the effect estimate. Regular inverse probability weights use the probability of being in the ...
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1answer
47 views
Using binary outcome variables in real-world data studies must be wrong?
Please be gentle if it's a stupid(ly easy) question:
In medical literature lot's of randomised clinical trials use binary outcome variables, such as 90% reduction in Y, or Y<(a certain threshold). ...
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1answer
88 views
Propensity score matching with MatchIt: Question on missing standard mean differences in balance tab on cobalt package
I am using MatchIt to carry out some propensity score matching, and then using the cobalt package to generate the balance diagnostic. The summary() command on MatchIt has a known bug where it does not ...
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0answers
43 views
IPW for the effect of treatment on treated with a continuous treatment
I've been banging my head against the wall trying to figure out how to construct inverse probability of treatment weights (IPTW) for the population average effect of treatment on treated (PATT) with a ...
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1answer
49 views
Why is Propensity Score called a distance measure as well?
I had to use Propensity score matching for my study. I used the MatchIt function in R and I studied what it actually does. I understand that the propensity score is calculated using Logistic ...
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0answers
20 views
Creating a Weighted Ratio Based On Size of Customer
I am attempting to created a weighted ratio/score for customers based on the number of support tickets they have entered in a time period against the number of units they have in service with us.
...
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1answer
35 views
Propensity score: which treatment effect is easier to infer?
I'm currently working on a study where the goal is to estimate the treatment effect of a binary exposure. I want to calculate the Average Treatment Effect (ATE), Average Treatment Effect in the ...
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1answer
272 views
Matching with Multiple Treatments
What's the best way to use matching methods with multiple treatment groups?
I'm assessing the impact of an intervention on an outcome. For my first analysis, I used the MatchIt package (see code below)...
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1answer
185 views
Difference between covariates and treatment confounders in propensity score matching
Here, I have the definition of a propensity score:
Propensity score is defined as the conditional probability of assignment to a treatment given a vector of covariates including the values of all ...
2
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1answer
359 views
Difference between using a propensity score for matching vs. regression analysis
So I am confused on what the difference is if I match patients based on propensity scores vs. using the propensity score and then applying that into a multivariate regression analysis? Is there a ...
0
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1answer
41 views
Is repeated propensity score matching over many 0-1-features a valid procedure?
I would like to do a simple linear model where the outcome $y$ is real-valued, but my data matrix $X$ consists of several hundred features that all are $0$-$1$-valued. The number of observations $n$ ...
2
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1answer
34 views
CBPS Package: PS changes by changing base category [closed]
I am using CBPS package. The treatment variables has three options. Therefore, a multi logit model is run through CBPS. However, the resulting propensity score changes by changing the base category.
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2
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1answer
93 views
Propensity Scores: What is this estimator?
I'm reading The Central Role of the Propensity Score in Observational Studies for Causal Effects in order to understand why Propensity Scores work. I'm kind of new to this and I'm not understanding an ...
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0answers
47 views
Posterior distribution of ATE with Bayesian propensity score model?
I am using propensity score weighting (PSW) to estimate the average treatment effect (ATE) of some treatment $D$ on an outcome $Y$ with covariates $X$. I have seen several ways in the literature (both ...
0
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1answer
150 views
Warnings during propensity-score matching: 1: glm.fit: algorithm did not converge 2: glm.fit: fitted probabilities numerically 0 or 1 occurred [duplicate]
I am doing a propensity score matching(nearest neighbor matching) in R with simulated data and I keep getting the above warning messages. please I need help.
The following is my code
...
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0answers
48 views
Why weighted glm coefficients differ from weighted mean?
Let's consider the following dataframe:
...
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1answer
407 views
A difference-in-differences propensity score matching approach
I am facing some challenges using the DID.I have around 500 Items off which 100 are test and its very difficult to find a control group for DID, so I used PSM to find control group using nearest ...
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0answers
104 views
Stabilized propensity weights: intuition and ATT formula
The average treatment effect (ATE) of binary treatment T on outcome Y can be estimated using inverse propensity weights:
\begin{equation}\nonumber
\frac{\sum_{i=1}^{N}t_i\hat{\pi}_i^{-1}y_i}{\sum_{i=...
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0answers
62 views
Propensity score matching and DD over different event windows
I'm currently working on a project in which we are trying to investigate the operating (accounting) performance of seasoned equity offerings.
To comprehend the endogeneity problem, we apply ...
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0answers
86 views
Impact of propensity model
I have built a propensity model, which gives out probabilities of a customer paying given a collection intervention using a xgboost model. The model has an AOC-ROC of 81% with an accuracy of 77% ...
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0answers
21 views
Variables to include in propensity score matching
I want to use propensity score matching to estimate how becoming a mother affect your health.
I define you as a mother, if you in 2006 have children who lives at your home.
Since paneldata i ...
1
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1answer
180 views
Is it correct to use paired t test after 1:2 matching?
Is it correct to use paired t test after 1:2 propensity score matching? How we can do this in R? I found the following link in SPSS (https://www.researchgate.net/post/how_is_paired_t-...
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0answers
30 views
Can selection bias be solved by including control variables?
Omitted variable bias can be solved by including covariates that are omitted. However, can selection bias also be solved by including covariates?
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1answer
142 views
Propensity Score Matching over time?
I have two surveys of households in the same metropolitan areas about the number of transit trips they took. I would like to compare the change in number of transit trips taken by households, between ...
