All Questions
Tagged with causal-inference or causality
1,890 questions
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How to find a de-biased estimator with a ML component in my contaminated data problem?
I am trying to use the output of a machine learning model to estimate (using a maximum likelihood approach) a parameter in a distribution. The estimator I get has a bias which is much larger than the ...
- 101
4
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1
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49
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IV Estimation, Direct and Indirect Effects
Suppose that I have a setup as follows, where $y_i$ is an outcome of interest, and $x_i$ is some endogenous regressor. The aim is to estimate $\beta$. Now suppose there is a variable $z_i$ that is ...
3
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1
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37
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RWE: use of target trial emulation framework: why aiming to emulate RCT?
Hernán and Robins [1] introduced the target trial emulation framework in 2016 to define the question of interest in observational studies. The target trial framework asks the investigator to specify ...
- 337
1
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0
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61
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Causal Discovery Packages Supporting Mixed Data Types and Prior Knowledge
I am new to the field of Causal Discovery and would like to apply standard algorithms to my dataset, which contains both categorical and continuous variables. I am looking for Python packages that can ...
- 63
2
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0
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30
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Multi-objective structural causal bandits
I am reading the paper "Structural Causal Bandits: Where to intervene?" by Lee & Bareinboim (2018). Here is a link: https://papers.nips.cc/paper_files/paper/2018/file/...
- 121
1
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1
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60
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Outcome Level vs. Treatment Level vs. Fixed-Effects Level in Difference-in-Differences
I am a bit confused about what controls should I include in my event-study (Callaway and Santanna 2021) specifications.
One of my models tries to understand the impact of the opening of a public ...
- 43
1
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0
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27
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Can my control and treatment groups have the same t=-1 value for the dependent variable in an event study framework?
I am running an event study (Callaway and Santanna 2021 framework) in which my dependent variable is binary (1 if the person is declared as homeless, 0 otherwhise). I want to see the impact of the ...
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1
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1
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39
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How to find causal inference when one subgroup of population, all are treated?
Suppose we have a dataset with different features, one of which is gender and based on the binary treatment, we observe the binary outcome (recovery). There is one issue within the dataset. Based on ...
- 693
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0
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18
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Substitution modeling under simulated inventory constraints
In the context of retail, substitution is typically defined as "when customers that prefer product A, but when unavailable, purchase product B, instead."
Naively, the most of useful ...
- 3,520
6
votes
1
answer
75
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Order in which covariates are measured in an observational study - causal inference
I want to model hba1c levels for a group of type 1 diabetes patients. I have data which are extracted from a register, and my goal is to answer whether a treatment intervention decreases hba1c levels ...
- 332
0
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0
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30
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Estimate impact of product placement when origin is unknown
I have general weekly sales data of several products in different stores for products that can be in many locations. The challenge is that there’s no way to track where exactly each customer picked up ...
0
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25
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I know this isn’t the right way to test causality but what would the right method be for this?
My first guess would be to some sort of interrupted time series or regression kink design. P.S. PDMP is prescription drug monitoring programs. Prescription rates is for opioids. The graphs of each are ...
- 347
5
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1
answer
54
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Isn't MNAR (missing not at random) just an unprovable hypothesis?
If participants drop out during the study after interventions or placebos are assigned, and we analyze the data based on those dropout outcomes, can we only identify associations rather than causality ...
- 151
5
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1
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50
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Can I do a difference-in-difference analysis if my outcome variable is nominal?
When the outcome variable is nominal and has more than two categories that are not in a specific order, can I still perform a difference-in-difference analysis?
I am interested coordination among ...
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0
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15
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How to choose a control group in Interrupted Time Series?
I have a dataframe similar to the following:
...
- 11
0
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0
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33
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Extracting continuous effects from a brms model after weighting using the marginaleffects package - avg_slopes() or slopes()?
I'd like to estimate the causal effect of the continuous variable perc_quality_plus_palms on the number of counts of different bird species for each combination of Forest.dependency (3 categories) and ...
- 31
1
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1
answer
36
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Why does the coefficient lose significance when using IV independent variable and also including district fixed effects?
I am trying to examine the effect of a 2018 flood shock on labor force participation of male and female. However, the flood could not only be attributed to heavy rainfall but to other factors like ...
2
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1
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315
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Efficient influence function with interventions that depend on the natural value of the exposure
Figure A1 shows a SWIG with L being a confounder of the association between exposure X and outcome ...
- 729
1
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1
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30
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Difference-in-Difference with treatment defined after event
I am working through a scientific paper which uses a difference-in-difference design, which, however, is not your standard DID setting.
Think about the case where we collect observations of multiple ...
- 387
2
votes
2
answers
86
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Definition of selection bias vs confounding bias
I've been learning about causal inference, having read Pearl's Primer and Parts I and II of "What If?".
I was under the impression that the definition of "There is confounding" was
...
- 225
3
votes
2
answers
71
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Limitations of propensity score matching
While studying propensity score matching, I was struck by the following thought:
When we are running a logistic regression model to estimate $p(Z=1∣X)$ through some form of parametrization and we are ...
4
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1
answer
94
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Equivalence of Two Latent Structures in Causal Inference
This is a claim made by Professor Judea Pearl in his classical monograph Causality: Models, Reasoning and Inference (footnote 5 in Section 2.1). For the two causal structures below (where "$(*)$&...
- 21.2k
2
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1
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77
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Natural Direct / Indirect Effect for continuous treatment - Causal Mediation
I'm reading in the topic of causal mediation and would like to apply it to a case with all continuous variables, treatment mediator interaction, and assuming linearity. I'd like to calculate the ...
- 281
4
votes
1
answer
47
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Good practice when estimating nuisance parameters for doubly robust effect estimation
I'm involved in a project that uses data from various medical registers to perform observational studies with the goal of treatment effect estimation. It is already decided that studies will by ...
- 128
2
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2
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45
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How to prove an inference cannot be drawn from an irrelevant test?
Is anyone aware of good sections in statistics literature or textbooks that explain why it is essential to make sure that the tests you are running are relevant to the question you are investigating? ...
0
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1
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31
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How to Model the contribution of a continuos treatement on a limited dependet binary variable, with only records for positive outcome
I'm trying to estimate the causal contribution of a continuous treatment variable (number of marketing touchpoints) over a binary outcome variable (opportunity is generated or not). The challenge ...
0
votes
1
answer
36
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Modeling approaches for conditional probability distribution, applied to Propensity Score estimation for IPW (causal inference)
I'm trying to understand and ideally implement the Inverse Probability Weighting approach to estimate a causal effect. My ressources so far have been Pearl's Primer and the book "What If?".
...
- 225
1
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1
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57
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Randomization and Causal Dags
Suppose we have Treatment T and Outcome O, and variables A and B, with the following causal dag:
But if we were doing an RCT with these treatment and outcome variables, is it accurate to think of the ...
- 125
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0
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15
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ATT in longitudinal model using generalized estimating equation in the marginaleffects package
I fit a GEE in R. I have an observational study with a dichotomous grouping variable and I was wondering how to calculate the ATT using the package Marginaleffects.
Lets say I have the model:
model &...
0
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0
answers
23
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Quantifying the causal effect of change in variability
Say there is a wood cutting machine. When we tune the machine the accuracy of the machine increases. Meaning the machine is supposed to cut $\mu$ inch blocks but has error $\epsilon$. The variability ...
- 704
3
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1
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285
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How to decide which spline to use when conducting g-computation after weighting?
I've conducted statistical weighting (R package WeightIt) and I am now using g-computation (R package marginal effects) to estimate the effect of % tree cover on crop yield (following https://ngreifer....
- 31
2
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1
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93
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Causal Effect in DAG with two alternative scenarios
I am trying to understand DAGs and I am not sure I am getting it right.
I hope somebody here can help.
Let's say I have the following scenario:
When the weather is good, the employees of a company ...
- 21
0
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0
answers
34
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Difference-in-Differences when treatment predates time of implementation (t1)
I was wondering if you believed the difference-in-differences design I have in mind is appropriate given that the treatment pre-dates t1.
I want to estimate the effect of public libraries established ...
- 183
3
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1
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46
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longitudinal regression but cohort is different in each year?
In most textbook examples for longitudinal regression, they describe situations where there is a group of people that is followed up over a period of time and measured. Sometimes people drop out of ...
- 31
0
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0
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17
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Are CATE/ITE estimates supposed to be normally distributed?
I am doing uplift modeling and predicting ITEs for a bunch of individuals. When I plot these, should I expected them to be normally distributed. One of the models I tried had the predictions be bi- or ...
- 1
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0
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9
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Resources to learn about designing and analyzing cross-over trials from the lens of DAGs and potential outcomes
I am looking for good resources to learn about the proper design and analysis of randomized trials to compare treatment sequences. In other words, trials compare drug A before drug B to drug B before ...
3
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1
answer
182
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Model specification for causal inference with longitudinal data and multilevel modeling
We studied the effect of media of delivery (i.e. augmented reality vs video) of educational content on memory (“binary_memory_score”) over time (“time”). We had 20 augmented reality experiences, with ...
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0
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2
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52
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Question about regression [duplicate]
Let's assume that I have large samples of data for the price and quantity of a product. I regress this data and I find that the correlation is one. Since the residuals in a linear regression are 0 ...
0
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0
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8
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Is simultaing the synthetic control dependent variables with the same independent variables a valid approach?
im doing a synthetic control and i have some data limitations.
im making a synthetic control for a city in country A (treated unit) and for simulating the control group i have data for multiple citys ...
0
votes
1
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38
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Uplift Modeling X-Learner - One model for all treatments or multiple models for each treatment?
I am using an x-learner (and doubly robust learner as well) for uplift modeling. I have a control group and 10 treatments. To start, I have just be creating one x-learner and passing it all the ...
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0
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0
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27
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Proper Difference-in-Difference Model for Time Variant Groups
Take the following example... I have two areas: Area A and Area B. Area A are individuals in a geographic area who are exposed to a health intervention. The health intervention is applied to the ...
- 238
7
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2
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686
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R Squared Causal Inference
I'm trying to know whether a low R-squared value would pose a problem when assessing the coefficients. My population is divided into two groups (A and B), and I want to assess if there's a significant ...
- 363
1
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1
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44
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Positivity assumption and Conditional exchangeability
Let's imagine a simple-traditional scenario: A <--- L ---> Y and A ---> Y. Thus, we have exposure A (treatment and no treatment) and outcome Y. L represents confounder(s). For my specific ...
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4
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1
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In a doubly robust learner, do the covariates need to be the same for the outcome model and the propensity model?
In a doubly robust learner/estimator, do we need to use the same feature set X when creating the outcome model and the propensity model? Or could we use a subset of X for the propensity model or even ...
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3
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1
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48
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Is distributions defining necessary for a DAG to be a causal bayesian network?
First, let's define the following abbreviations: Directed Acyclic Graph (DAG), Bayesian Network (BN), Causal Bayesian Network (CBN), Conditional Probability Table (CPT), Conditional Probability ...
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5
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1
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298
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Clarification on Counterfactual Outcomes in Causal Inference
I’m studying the textbook Causal Inference: What If by Miguel A. Hernán, James M. Robins. On page 4, I came across a passage that seems nonsensical. The authors claim that, for each individual, the ...
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2
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2
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94
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Simple OLS to measure correlation
I have two variables, X and Y, and I have good reason to believe that they are simultaneously determined.
$$Y = a_{1} + b_{1}X + u_{1}\tag{1}$$
$$X = a_{2} + b_{2}Y + u_{2}\tag{2}$$
My question is ...
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2
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3
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72
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Finding the true "causative" covariate - in joint versus separate modeling
This is regarding some genetic assignment.
Assume we have two random covariates (SNPs) $X1,X2$, and a random response $Y$ (disease). I believe that only one of $X1,X2$ is “causative” for $Y$ ,
but do ...
1
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0
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26
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How to account for imbalanced data and pre-treatment exposure in an impact evaluation?
I am trying to evaluate the impact of a tutoring intervention and have test score data for treated children and a control group. However, study enrolment began earlier for the treatment group than the ...
- 151
1
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1
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56
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Implications of using a caliper when matching: Would it ruin ATE claim?
I've started to learn about matching methods using MatchIt package, and read "Choosing the causal estimand for propensity score analysis of observational studies" (Greifer & Stuart, 2021)...
