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Questions tagged [selection-bias]

Bias introduced by non-random selection of observations, such that the sample is not representative of the underlying population.

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3 answers
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Is prescreening not detrimental for paid surveys?

Survey sites like Swagbucks have often a prescreening mode in which one is asked questions like your annual income, whether you own car or not. It is observed that most of the time if one selects ...
Splendid Digital Solutions's user avatar
1 vote
0 answers
15 views

Inverse Probability of Weighting in Directed acyclic graph for a binary collider as a selection bias

For a confounder, like the following figure, it is commonly suggested that use of the Inverse Probability of Weighting can remove the path from confounder to exposure so that it removes the backdoor ...
Elong Chen's user avatar
3 votes
1 answer
39 views

Statistical Non-Response and Drop Out

In statistical studies, it is possible that there might be biases: Someone groups of people are more likely to be represented compared to others groups of people (e.g. poorer people have difficult ...
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Positivity Assumption in Propensity Score Methods for Pre- and Post-Treatment [duplicate]

I am designing a research project and could use some guidance. My research question focuses on estimating the effect of a new co-responder policing program on use-of-force and arrests. I want to see ...
galaxy-friday1017's user avatar
0 votes
1 answer
33 views

conditional-on-positives bias

I am reading the Bad COP section on https://matheusfacure.github.io/python-causality-handbook/07-Beyond-Confounders.html#bad-cop. I am confused if $$ E[Y|T = 1] - E[Y|T = 0] = \\ E[Y|Y > 0, T = 1]...
Anonny's user avatar
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1 vote
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Regression Discontinuity Design, staggered treatment allocation

I'm unsure if this complex allocation rule is appropriate for RDD. I will have data for a staggered rollout treatment where there will be about 10 rounds of selection over two years for services (...
dcoy's user avatar
  • 362
1 vote
1 answer
34 views

Bias introduced by removing early censors

Suppose we have right-censored survival data on some population, and want to compare individuals with "good outcome" (who have no event in the first X months) to individuals with "bad ...
Nuclear Hoagie's user avatar
0 votes
4 answers
114 views

Small-sample binary logit and linear models - response to referees [closed]

Background: This cross-sectional study collected 30 thrombosis samples. We evaluated the presence or absence of MP components (dependent variable), where 24 cases had MP (coded as 1) and 6 cases did ...
zhiheng yi's user avatar
1 vote
1 answer
42 views

Average treatment effect (ATE) estimation via matching method while outcomes of control population are constant

I want to estimate the average effect of a treatment that was given with a selection bias. To do this, I'd like to use a matching method. Basically, this method involves finding, for each treated ...
HnbBarca's user avatar
0 votes
0 answers
35 views

Find correlation from biased observations

I have a set of observations of a variable Z (shown as the colormap) as a function of two other variables A and B. I want to study how Z varies with respect to A, B, and both A and B (eg. if A ...
Euryproktos's user avatar
2 votes
0 answers
35 views

How to estimate the age of players correctly?

I have the data of players active on a gaming console and the playtime hours corresponding to the games they have played and their age. I want to analyze the top (say 10) games that the people between ...
Ritik P. Nayak's user avatar
3 votes
1 answer
47 views

Deriving conditional independence statements for causal graphs with selection nodes

In "basic" causal graphs / DAGs / probabilistic graphical models (PGMs), conditional independence statements can be derived using the d-separation criterion. How does this work if selection ...
Eike P.'s user avatar
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1 vote
1 answer
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Choice and endogeneity

An independent variable is endogenous if it is correlated with the error term (source). In the regression framework, this may happen (only?) in case of omitted variables, simultaneity, or measurement ...
robertspierre's user avatar
0 votes
0 answers
33 views

Difference in Difference and Selection Into Treatment

Suppose I impose that the true model of some variable of interest is: $$ Y_{it} = \alpha_i + \beta_t+\tau_{it}D_{it}+\epsilon_{it} $$ Where $ D_{it} = 1\{E_i \geq t\} $. This is a kind of DID model ...
DarkenExcalibur's user avatar
1 vote
0 answers
186 views

Calculating Inverse Mills Ratio after Probit

I need to compute the Inverse Mills Ratio after the probit command in Stata. From here, I found that predict IMR1, score, will calculate it and store it in IMR1. I ...
user917983's user avatar
5 votes
2 answers
207 views

Correcting for selection bias with standardisation/g-computation

Two sets of methods for correcting for selection bias are g-computation (standardisation) and inverse probability of censoring weighting (IPCW). I'm having a difficult time understanding how to apply ...
Lachlan's user avatar
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0 votes
0 answers
21 views

Right Way to Sample a Validation Set

I am working on a project that uses training data selection techniques; it involves sampling the training set in some smart way rather than sampling randomly. The goal is to compare different data ...
Mr.Robot's user avatar
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1 vote
1 answer
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Understanding selection bias and endogeneity in marketing

Media mix modelling is concerned with estimating causal impact of marketing investments , a goal which have several challenges. In general, multiple regression models are deployed mapping up total ...
kurt eriksson's user avatar
0 votes
0 answers
31 views

Is "skewing the data" and "skewing the results" just selection bias?

I recall various conversations with biologists, ecologists, and foresters that I neglected to ask for clarification on at the time. It doesn't occur in any of my statistics references. Sometimes in ...
Galen's user avatar
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0 votes
0 answers
9 views

How to improve sample representativeness for longitudinal data collected via an online platform?

I am working with a longitudinal dataset exploring cognitive ageing (e.g., memory performance over time). Participants complete the study annually. Inclusion criteria for this study are 1) UK resident,...
AEP_Psych's user avatar
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1 vote
0 answers
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Can I use Shapley values with metadata (i.e. information about observations that I didn't train my model on)?

I'm training a set of models (random forest/XGBoost) for an ordinal regression task. I'm (tentatively) planning to use Shapley values to infer feature performance. I also have some metadata that my ...
Neil's user avatar
  • 66
1 vote
0 answers
31 views

How can aggregation be helpful in mitigating bias?

I am working on the estimation assessing the impact of exposure to infrastructure (mainly schooling) on the number of children. Since I do not have migration data, my colleague recommended that I ...
Yendao Su's user avatar
1 vote
1 answer
32 views

Selection Bias in Conflict Studies

A common critique I have heard levied against conflict studies (research examining the causes, consequences, and solutions to violence such as civil war, terrorism, etc.) is the problem of selection ...
Brian Lookabaugh's user avatar
2 votes
0 answers
714 views

How to address selection bias in a diff-in-diff study?

We know that selection bias occurs when the treatment and control groups are not comparable, leading to differences in the outcome that are not solely due to the treatment. First edit: By selection ...
funcard's user avatar
  • 61
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0 answers
69 views

Heckman correction for correlation estimates

Suppose I observe random $y_{i,1}, y_{i,2}$, and I wish to estimate the correlation between them. However, the $y_{i,j}$ are observed subject to some sample selection criterion. That is, there are ...
shabbychef's user avatar
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1 vote
1 answer
36 views

Comparing a multi-dose drug to no drug exposure in a cohort study: Censoring events between doses

I am interested in assessing the association between the two doses of a dietary supplement on an event of interest. The primary exposure is 'two doses of the supplement', and the comparator is 'no ...
user3qpu's user avatar
  • 109
0 votes
1 answer
107 views

How to understand random assignment eliminates selection bias in the potential outcomes framework

In Angrist & Pischke's book mostly harmless econometrics, they explain that if the treatment in an RCT $D_i$ is randomly assigned, then $D_i$ is independent of potential outcomes and the following ...
Tomas R's user avatar
  • 177
1 vote
0 answers
18 views

Selection bias in postmortem data and creating an artificial earlier study endpoint

I want to analyze postmortem (neuropathology) data from dementia patients who are part of a larger ongoing observational study. At the time of the data freeze (i.e. the time at which I access the data)...
AnnaC's user avatar
  • 11
0 votes
1 answer
94 views

Questions Regarding Sampling Bias

I'm taking a course in R: "Data Analysis in R" on Coursera, and I came across this question during the lecture: A retail store considering updates to their credit card policies randomly ...
JackJackAttack0214's user avatar
2 votes
1 answer
60 views

How to correct for sampling bias in one population when comparing against another

I have two populations that I'd like to compare across certain metrics. However, most members of population A did not respond to our request for data, and those respondents that did are not ...
mdrishan's user avatar
  • 207
1 vote
1 answer
51 views

Maximum likelihood of Normal density under selection

Consider the density function given by $$ \left[\dfrac{\gamma_{\leq0} \mathbb{1}(t \leq 0) + \gamma_{>0} \mathbb{1}(t > 0)}{\gamma_{\leq0}\Phi\left(- \mu / \sigma\right) + \gamma_{>0}\Phi\...
Student_718's user avatar
1 vote
0 answers
46 views

Why does normalizing difference score>0.25 indicates selection bias which cannot be corrected by regression?

I am reading Propensity Score Analysis(2014) by Guo and Fraser chapter 1 section 4. Denote $\Delta_X$ normalizing difference score of covariate $X$. "Following Imbens and Wooldridge, a $\Delta_X$ ...
user45765's user avatar
  • 1,445
1 vote
0 answers
130 views

Inverse Mills Ratio Interpretation [closed]

What is the interpretation of inverse mills ratio in Heckman Selection Model ? Why we are including it as an explanatory variable in the OLS estimator?
Shivam Saboo's user avatar
1 vote
1 answer
24 views

Control Group Selection Bias

I found a study that compared minor physical anomalies(MPA) between certain group of patients with the control group to determine if MPAs occur more frequently among these patients compared to the ...
Kim's user avatar
  • 11
1 vote
1 answer
19 views

Estimating interactions from non-interacting features

Suppose I have a sample $\mathcal{D}=\{(\mathbf{x}^{i}, y^{i})\}_{i=1\dots M}$ of binary variables $\mathbf{X}$ ($N$ of them) and a continuous variable $Y$ that I want to predict based on a linear ...
Sergio's user avatar
  • 336
1 vote
0 answers
94 views

Nested Cross-Validation with Small dataset

I am currently working with a small dataset (only 175 samples, 45 features) and have been reading on the proper way to cross-validate my model. I had started with a basic cross-validation using a grid ...
Fritos121's user avatar
2 votes
1 answer
269 views

Sampling weights in Cox proportional hazards models

I'd like to use sampling weights in a Cox proportional hazards regression model to address selection due to different response probabilities. I'm calculating the weights as Inverse Probability of ...
r_epi's user avatar
  • 31
4 votes
3 answers
164 views

When can we get unbiased estimate given biased data?

There was a recent "hot take" tweet by Andrej Karpathy (without any comment or clarification from the author): real-world data distribution is ~N(0,1) good dataset is ~U(-2,2) It provoked ...
Tim's user avatar
  • 140k
3 votes
1 answer
319 views

Selection Models of Publication Bias for Multilevel Meta-analyses?

Are there any suitable selection models of publication bias for multilevel meta-analyses? I am currently conducting a 3-level meta-analysis and trying to incorporate selection models to assess ...
makie's user avatar
  • 73
1 vote
1 answer
26 views

Sample Bias in Study

I have following Study statement: A council wishes to study the digital awareness of its resident senior population (over 65 years), so it questioned in person 50 residents randomly chosen from a ...
Snoke's user avatar
  • 23
1 vote
1 answer
83 views

How to show how biased a statistic is in a non-random sample, knowing the parameter in the general population?

I have a convenience sample, and want to show readers how biased it is relative to the population it's taken from. It's absolutely certain that the sample is biased, and I want to give readers as many ...
J-J-J's user avatar
  • 4,964
1 vote
1 answer
111 views

Is it true that a larger, representative dataset is always better to use than a smaller, representative dataset?

By "representative" I mean that the data in the dataset faithfully reflects the "underlying signal" a model is trying to tap in to. Is it always true that, as long as increasing ...
sangstar's user avatar
  • 131
0 votes
1 answer
28 views

Can I ignore these individuals without introducing bias?

I have a population that falls under 10 classes. Each individual may or may not come with a location - 83% overall have locations and a breakdown by class is: Class # individuals # with location # ...
Chris Browne's user avatar
2 votes
0 answers
66 views

What are the statistical fallacies of illusion of control?

Illusion of control* appears in gambling and events involving randomness. For example, choosing a lottery ticket which has an additional information that participant has a control of choosing, such as ...
patagonicus's user avatar
  • 2,550
1 vote
1 answer
28 views

Estimating population means after a selection procedure

The problem is somewhat related to sequential procedures (e.g. Paulson; Gupta and Miescke) I have a hundred engines generating normal random numbers with true (unknown) means $E_1,E_2,E_3...E_{100}$, ...
PKV's user avatar
  • 13
1 vote
0 answers
28 views

Looking for a type of bias between "Selection bias" and "Framing"

I am looking for a word to describe a certain type of bias, something between Framing and Selection Bias. I am analyizing a dataset with measurements and their location in Berlin. The dataset includes ...
C4X's user avatar
  • 11
2 votes
0 answers
73 views

effect of vaccination against covid on hospitalization - help building a causal DAG [closed]

During the covid crisis, we have seen explanations of why vaccinated people end up less hospitalized than unvaccinated people under the paradox that there were more vaccinated people hospitalized than ...
Tanguy's user avatar
  • 465
2 votes
0 answers
148 views

How to accommodate endogeneity after matching?

I am working on a field experiment where assignment to treatment vs. comparison was random, but participation uptake was not. The design is pre-post, and attrition is certainly not MCAR. This is a ...
Patrick Malone's user avatar
2 votes
1 answer
696 views

What is the difference between selection bias and composition effect?

Groups we seek to compare (e.g., a treatment group and control group) may differ in ways that constrain our ability to do so. Often, potential outcomes may differ systematically across groups, such as ...
Dr. Beeblebrox's user avatar
10 votes
3 answers
2k views

What is it called when an experimenter discards results that are too unexpected?

There is a type of scientific error where an experimenter gets a result significantly different from prior researchers, assumes they made a mistake, and redoes the experiment until they get a more ...